Claims:
A method of calibrating a weigh-in-motion (WIM) device configured for determining dynamic weight of a moving vehicle, the method comprising:
defining a multi-dimensional calibration space for the WIM device, the multi-dimensional calibration space implementing percentage error values versus at least one of distinct deviating factors affecting accuracy of the WIM device;
determining at least two percentage error values in the multi-dimensional calibration spacefor the WIM device by moving a test vehicle thereon while varying one of the distinct deviating factors and measuring instantaneous readings thereof, and comparing the measured instantaneous readings with corresponding pre-determined readings of the respective one of the distinct deviating factors for the test vehicle;
estimating other of requisitenumber of percentage error values in the multi-dimensional calibration space for the WIM device based, at least in part, on the determined at least two percentage error values, for the varied one of the distinct deviating factors; and
setting calibration coefficients for each point in the defined multi-dimensional calibration space for the WIM device based on the determined at least two percentage error values and the estimated other of requisite number of percentage error values.

The method as claimed in claim 1 further comprising, prior to determining at least two percentage error values, setting up the WIM device by:
determining a zero-dimensional calibration coefficient by measuring instantaneous reading of the WIM device with no load thereon; and
determining a one-dimensional calibration coefficient by statically applying one or more known weights to the WIM device and correlating corresponding readings thereof.

The method as claimed in claim 1, wherein the at least one of distinct deviating factors comprises speed of the moving vehicle, axleweight of the moving vehicle and direction of travel of the moving vehicle.

The method as claimed in claim 1, wherein estimating other of requisite number of percentage error values utilizes one or more of predictive interpolation and extrapolation techniques.

The method as claimed in claim 1, wherein estimating other of requisite number of percentage error values utilizes the least square curve fitting technique.

The method as claimed in claim 1 further comprising allowing manual manipulation of calibration coefficients.

The method as claimed in claim 1 further comprising providing a security layer to limit access for manipulating the set calibration coefficients for the WIM device.

The method as claimed in claim 7 further comprising sending a notification to predefined authority if any of the set calibration coefficients for the WIM device is manipulated.

A method of measuring dynamic weight of a moving vehicle based on claim 1, the method comprising:
determining appropriate calibration coefficient based on the distinct deviating factors of the moving vehicle; and
applying the appropriate calibration coefficient in an equation:
W_corr=W_meas×(1-(E_d (s,w))/100),
wherein, ‘Wcorr’ is the corrected axle weight, ‘Wmeas’ is the measured axle weight, and ‘Ed(s,w)’ is the calibration coefficient for direction ‘d’, speed slice ‘s’ and weight slice ‘w’ in the multi-dimensional calibration space for the WIM device.

A system for calibrating a weigh-in-motion (WIM) device configured for determining dynamic weight of a moving vehicle, the system comprising:
a controller embedded in the WIM device and adapted to store calibration coefficients therefor, the controller configured to generate digital signals corresponding to measured instantaneous readings by the WIM device; and
a computing device in communication with the controller to receive generated digital signals corresponding to measured instantaneous readings by the WIM device and adapted to have access to change the stored calibration coefficients in the controller, the computing device configured to:
define a multi-dimensional calibration space for the WIM device, the multi-dimensional calibration space implementing percentage error values versus at least one of distinct deviating factors affecting accuracy of the WIM device;
determine at least two percentage error values in the multi-dimensional calibration space for the WIM device by moving a test vehicle thereon while varying one of the distinct deviating factors and measuring instantaneous readings thereof, and comparing the measured instantaneous readings with corresponding pre-determined readings of the respective one of the distinct deviating factors for the test vehicle;
estimate other of requisite number of percentage error values in the multi-dimensional calibration space for the WIM device based, at least in part, on the determined at least two percentage error values, for the varied one of the distinct deviating factors; and
set calibration coefficients for each point in the defined multi-dimensional calibration space for the WIM device based on the determined at least two percentage error values and the estimated other of requisite number of percentage error values.

The system as claimed in claim 10, wherein the computing device is further configured to:
determine a zero-dimensional calibration coefficient by measuring instantaneous reading of the WIM device with no load thereon; and
determine a one-dimensional calibration coefficient by statically applying one or more known weights to the WIM device and correlating corresponding readings thereof.

The system as claimed in claim 10, wherein the at least one of distinct deviating factors comprises speed of the moving vehicle, axle weight of the moving vehicle and direction of travel of the moving vehicle.

The system as claimed in claim 10, wherein the computing device is further configured to utilize one or more of predictive interpolation and extrapolation techniques for estimating other of requisite number of percentage error values.

The system as claimed in claim 10, wherein the computing device is further configured to utilize the least square curve fitting technique for estimating other of requisite number of percentage error values.

The system as claimed in claim 10, wherein the computing device is further configured to allow manual manipulation of calibration coefficients.

The system as claimed in claim 10, wherein the computing device is further configured to provide a security layer to limit access for manipulating the set calibration coefficients for the WIM device.

The system as claimed in claim 10, wherein the computing device is further configured to send a notification to predefined authority if any of the set calibration coefficients for the WIM device is manipulated.

A weigh-in-motion (WIM) device for measuring dynamic weight of a moving vehicle based on claim 10, wherein the controller in the WIM device is configured to:
determine appropriate calibration coefficient based on the distinct deviating factors of the moving vehicle; and
apply the appropriate calibration coefficient in an equation:
W_corr=W_meas×(1-(E_d (s,w))/100),
wherein, ‘Wcorr’ is the corrected axle weight, ‘Wmeas’ is the measured axle weight, and ‘Ed(s,w)’ is the calibration coefficient for direction ‘d’, speed slice ‘s’ and weight slice ‘w’ in the multi-dimensional calibration space for the WIM device.

A weigh-in-motion (WIM) apparatus incorporating the system of claim 10 and the WIM device of claim 18.

A computer program product residing on a computer readable storage medium of a computing device and having a plurality of instructions stored thereon which, when executed on one or more processors of the computing device, causes the computing device to perform operations for calibrating a weigh-in-motion (WIM) device configured for determining dynamic weight of a moving vehicle, the operations comprising:
defining a multi-dimensional calibration space for the WIM device, the multi-dimensional calibration space implementing percentage error values versus at least one of distinct deviating factors affecting accuracy of the WIM device;
determining at least two percentage error values in the multi-dimensional calibration space for the WIM device by moving a test vehicle thereon while varying one of the distinct deviating factors and measuring instantaneous readings thereof, and comparing the measured instantaneous readings with corresponding pre-determined readings of the respective one of the distinct deviating factors for the test vehicle;
estimating other of requisite number of percentage error values in the multi-dimensional calibration space for the WIM device based, at least in part, on the determined at least two percentage error values, for the varied one of the distinct deviating factors; and
setting calibration coefficients for each point in the defined multi-dimensional calibration space for the WIM device based on the determined at least two percentage error values and the estimated other of requisite number of percentage error values.
, Description:FIELD OF THE PRESENT DISCLOSURE
The present disclosure generally relates to a method for accurately determining dynamic weight of a moving vehicle, and more particularly relates to calibrating a weigh-in-motion (WIM) device built into a road or a track suitable for determining the dynamic weight of a moving vehicle, such as a truck, a train, and the like, as the vehicle is passing over.

BACKGROUND
Traditionally, truck weighing has been carried out using small, portable wheel scales carried by law enforcement personnel or vehicle weight survey specialists. The availability of truck weight, dimension, classification and speed data is required for highway engineers in determining the structural and maintenance requirements of pavements and bridges. Accurate data of these types is also necessary for planning, economic and enforcement surveys. More elaborate and safer weighing procedures are associated with fixed weigh stations located adjacent major highways and providing truck access by on-and off-ramp structures. Such weighing devices are static in that the vehicle is stopped and at rest during the procedure, which is inefficient, time-consuming and cumbersome.
Nowadays, dynamic weighing or weigh-in-motion (WIM) devices are being employed throughout the world for coping with the need to increase the throughput of the weighing operation. This is achieved by rapidly sampling the mV output of the weight sensor placed in the direct path of motion of the object being weighed. An algorithm then processes the data in the captured samples of each axle to extract the weight of the object. The weight sensor can be strain based devices like load cells, bending plates or piezo/quartz based devices.Such WIM devices are employed in numerous application including, but not limited to, moving for weighing road vehicles in motion and measuring axle loads, for automatic rail-weighbridges, for conveyor belt mass aggregator, inline process check-weigh devices, and the like.
WIM devices for road vehicles are becoming necessary with the dramatically rising costs for maintaining the traffic infrastructure, and safety of the road users. Accurate vehicle load data is vital for ensuring that highway systems remain intact and safe. Continuous overload detection is an important means for restraining overloading operators, often more efficient than punctual enforcement actions. This is possible by installing these devices in each lane of the toll plaza across the country. WIM scale devices is minimally invasive when compared to large, in-ground platform truck scales.Large static scales require incoming drivers to wait in line until the scale becomes available prior to entry, limiting throughput. Similarly, overloading can damage rail infrastructure and wagons, and un-even loading can lead to derailments. Additionally, the weight of wagons is required to collect revenue from shipping companies. Also, WIM devices may be required to detect shortfalls or leakage of goods immediately upon arrival at the depot. An in-motion scale makes weighing quicker and safer by eliminating the need to uncouple each rail car and position it on a static scale.
Metrological laws and specifications are based on static weight measurements. An instrument, such as the WIM device, is adjusted to be within the specification by a process called calibration. Calibrating the WIM device can be accomplished by using stationary loads being applied to the sensors. Field calibration procedures utilize vehicle of a known weight/configuration or a random sample of vehicles from the traffic stream measured using both a WIM system and vicinity static scale to determine mean differences between the WIM system and known/static scale measurements. The WIM system is then adjusted until mean differences equate to zero. This approach may be feasible for some WIM sites, but clearly does not take into account the pavement roughness induced load excitation at a particular WIM site. For railroad track scale where the strain gauge is bonded onto the tracks, placing standard weights might not be possible. The effects of track deficiencies, gradients and other factors influencing the determination of the static weight form the dynamic measurement will also not be factored in. As such, traditional WIM system calibration methods require conversion of the true dynamic load to a static measure, with an associated loss in accuracy.
In practice a WIM system is calibrated to replicate, as closely as possible, the static weights of most typical vehicles at their most typical operating characteristics. Basically, the intent of initial calibration is to use WIM readings from one or more test vehicles with known dimensions and axle weights as a basis for adjusting the WIM device's parameters so that the WIM readings match, within reasonable limits, the actual measurements. It is widely accepted that calibrating a WIM device to a single test truck does not ensure that the device will replicate static weights of all trucks in the traffic stream. Using more than a few test trucks for calibration limits the bias created from the use of a particular truck type. In this case a better statistical average is obtained the error due to the bias is distributed among the varied types used in the calibration. However, it is generally infeasible to associate the calibration values with a type of a vehicle.
Similar challenges exist for calibrating a railroad weigh scale which may take considerable time, often more than a single day, leading to a significant cost. To calibrate a coupled in-motion weighing track scale, 15 loaded reference cars or 10% of the number of cars that comprise trains normally weighed, whichever is greater, are required.Reference cars with mixed load cover the range from minimum to maximum load such that no interval between load bands is greater than 20% of the maximum wagon weight. The test trains are run over the scale at least 3 times in each manner of use (i.e. pushing/pulling, directions, etc.). The scale is tested in each manner of use up to 10 times at different speeds according to the regulations. Individual wagon weight accuracies are expected irrespective of speed of travel. Calibration for achieving accuracy across such varied influencing parameters can be a daunting task.
Calibration factors are associated with vehicle class and speed.WIM systems, regardless of application, either perform test vehicle runs using the site median traffic speed or the posted speed limit. A few WIM systems allow adjustment factors for multiple speeds. The method for computing calibration factors is split among vendor software and short-hand calculations. For systems which do not provide for any speed point adjustments, worksheets are used to adjust the WIM weight factor to be accurate for the speed range at which most of the truck traffic travels. Studies have shown that for systems supporting speed correction factors, a significant percentage of technicians’ reports inputting their average value in all speed bins after calibration. This may be due to the complexity involved in the computation and interpolation of the correction factors.
Furthermore, considering the commercial application of the product, where the weight is associated with a financial transaction, it is a legal requirement that the WIM device equipment is certified as ‘legal for trade’.This is governed by related legislation and administered, for instance, in United States, by the Weights and Measures (Legal Metrology) department. The legal metrology regulations prohibit the calibration of the WIM devices unless an officer of the department physically inspects and certifies the machine for accuracy. That is, the certification can only be given by a weights and measures inspector. Alteration of the equipment after it has been stamped is illegal. Most national legislation mandates the securing of components, interfaces, software and pre-set controls that change the parameters of measurement results, particularly for correction and calibration. The means of securing are required to be provided by hardware, passwords or similar means. By means of event counters and event loggers a metrological audit trail can be established in the indicator. However, the administration of these are still not with the Legal Metrology department but vest with the OEM and hence are subject to artful deceit to conceal wilful manipulation for gain.
Thus, traditional calibration methods used for static weighing are inadequate for dynamic weighing owing to the varied weighing conditions (speed, weight, direction). It is neither practical nor effective to attempt static weighing of a large sample of random vehicles from the traffic stream to calibrate a WIM device. Therefore, as the number of points for calibration explodes, a new method of calibration is required that is not too tedious to implement in the field and also abstracts the entire procedure leading to a more accurate system. Furthermore, there is a need to provide means to prevent tampering with the set calibration factors for the WIM device without proper legal compliance.
Documents describing the closest subject matter provide for a number of more or less complicated features that fail to solve the problems described above in an efficient and economical way. None of the documents suggest the novel features of the present disclosure.

SUMMARY
Numerous factors have an effect on performance and accuracy for dynamic weighing of a moving vehicle. For example, when a vehicle (such as, truck or wagon) is moving along a surface, its suspension becomes dynamically excited and instantaneous changes occur in the wheel and axle loads. The magnitude of the changes is related to the speed of travel. Secondly, the magnitude of the dynamic impact generated at the interface between the approach and the weighing surface is speed dependent. These combine to produce complex oscillatory weight components that are superimposed on top of the static load component to be measured. Further, site topography on either side of the weighbridge can affect the weighing performance of a road weighbridge.To obtain the best accuracy from a weighbridge that is used to weigh road vehicles the weighbridge should be installed in a controlled weighing area. Also for rail in motion, in general, the best weighing accuracy is achieved when the wagon couplings are stretched and the worst accuracy when the wagons are tightly buffered. Moreover, the site topography for approximately a train length either side of the weighbridge can have a profound effect on the weighing performance of a rail weighbridge. For instance, the gradient should not exceed more than 1:400 (0.25%) on either side of automatic rail weighbridge as steep gradients may cause heavy buffering or excessively large coupling forces, and the radius of curvature for bends should not be less than 250 m as tight track bends impede the free running of wagons and increase the coupling forces. Both of these may cause weight to transfer from adjacent wagons on to the wagon being weighed. Further, track deficiencies can have a profound effect on the accuracy of weighing. The most common shortcomings are low vertical stiffness, loose rails and excessively large gaps in fish plated joints, particularly low vertical stiffness resulting in large track deflections under load. The causes of this may be broken sleepers, migration of the ballast, a bad track bed, or poor drainage. Ideally track deflections should not be more than 5 mm under maximum axle load conditions and this is achievable with tracks constructed and maintained to Grade 1 main line standards. Furthermore, long trains give rise to large coupling or draw bar forces. The forces are greatest at the head of the train and decrease progressively towards the back. Coupling forces resolve into vertical and horizontal force components with the vertical component causing the weighing error. It may be appreciated that larger the coupling force the larger the error.
According to an exemplary aspect of the present disclosure, a method of calibrating a weigh-in-motion (WIM) device configured for determining dynamic weight of a moving vehicle is disclosed. The method comprises defining a multi-dimensional calibration space for the WIM device. The multi-dimensional calibration space implements percentage error values versus at least one of distinct deviating factors affecting accuracy of the WIM device. The method further comprises determining at least two percentage error values in the multi-dimensional calibration space for the WIM device by moving a test vehicle thereon while varying one of the distinct deviating factors and measuring instantaneous readings thereof, and comparing the measured instantaneous readings with corresponding pre-determined readings of the respective one of the distinct deviating factors for the test vehicle. The method further comprises estimating other of requisite number of percentage error values in the multi-dimensional calibration space for the WIM device based, at least in part, on the determined at least two percentage error values, for the varied one of the distinct deviating factors. The method further comprises setting calibration coefficients for each point in the defined multi-dimensional calibration space for the WIM device based on the determined at least two percentage error values and the estimated other of requisite number of percentage error values.
In an embodiment, the method further comprises, prior to determining at least two percentage error values, setting up the WIM device by determining a zero-dimensional calibration coefficient by measuring instantaneous reading of the WIM device with no load thereon; and determining a one-dimensional calibration coefficient by statically applying one or more known weights to the WIM device and correlating corresponding readings thereof.
In an embodiment, the at least one of distinct deviating factors comprises speed of the moving vehicle, axle weight of the moving vehicle and direction of travel of the moving vehicle.
In an embodiment, estimating other of requisite number of percentage error values utilizes one or more of predictive interpolation and extrapolation techniques.
In an embodiment, estimating other of requisite number of percentage error values utilizes the least square curve fitting technique.
In an embodiment, the method further comprises providing a security layer to limit access for manipulating the set calibration coefficients for the WIM device.
In an embodiment, the method further comprises sending a notification to predefined authority if any of the set calibration coefficients for the WIM device is manipulated.
According to another exemplary aspect of the present disclosure, a system for calibrating a weigh-in-motion (WIM) device configured for determining dynamic weight of a moving vehicle is disclosed. The system comprisesa controller embedded in the WIM device and adapted to store calibration coefficients therefor. The controller is configured to generate digital signals corresponding to measured instantaneous readings by the WIM device. The system further comprisesa computing device in communication with the controller to receive generated digital signals corresponding to measured instantaneous readings by the WIM device and adapted to have access to change the stored calibration coefficients in the controller. The computing device is configured to define a multi-dimensional calibration space for the WIM device, the multi-dimensional calibration space implementing percentage error values versus at least one of distinct deviating factors affecting accuracy of the WIM device; determine at least two percentage error values in the multi-dimensional calibration space for the WIM device by moving a test vehicle thereon while varying one of the distinct deviating factors and measuring instantaneous readings thereof, and comparing the measured instantaneous readings with corresponding pre-determined readings of the respective one of the distinct deviating factors for the test vehicle; estimate other of requisite number of percentage error values in the multi-dimensional calibration space for the WIM device based, at least in part, on the determined at least two percentage error values, for the varied one of the distinct deviating factors; and set calibration coefficients for each point in the defined multi-dimensional calibration space for the WIM device based on the determined at least two percentage error values and the estimated other of requisite number of percentage error values.
In an embodiment, the computing device is further configured to determine a zero-dimensional calibration coefficient by measuring instantaneous reading of the WIM device with no load thereon; and determine a one-dimensional calibration coefficient by statically applying one or more known weights to the WIM device and correlating corresponding readings thereof.
In an embodiment, the at least one of distinct deviating factors comprises speed of the moving vehicle, axle weight of the moving vehicle and direction of travel of the moving vehicle.
In an embodiment, the computing device is further configured to utilize one or more of predictive interpolation and extrapolation techniques for estimating other of requisite number of percentage error values.
In an embodiment, the computing device is further configured to utilize the least square curve fitting technique for estimating other of requisite number of percentage error values.
In an embodiment, the computing device is further configured to provide a security layer to limit access for manipulating the set calibration coefficients for the WIM device.
In an embodiment, the computing device is further configured to send a notification to predefined authority if any of the set calibration coefficients for the WIM device is manipulated.
According to yet another exemplary aspect of the present disclosure, a computer program product residing on a computer readable storage medium of a computing device and having a plurality of instructions stored thereon which, when executed on one or more processors of the computing device, causes the computing device to perform operations for calibrating a weigh-in-motion (WIM) device configured for determining dynamic weight of a moving vehicle is disclosed. The operations comprise defining a multi-dimensional calibration space for the WIM device. The multi-dimensional calibration space implements percentage error values versus at least one of distinct deviating factors affecting accuracy of the WIM device. The operations further comprisedetermining at least two percentage error values in the multi-dimensional calibration space for the WIM device by moving a test vehicle thereon while varying one of the distinct deviating factors and measuring instantaneous readings thereof, and comparing the measured instantaneous readings with corresponding pre-determined readings of the respective one of the distinct deviating factors for the test vehicle. The operations further compriseestimating other of requisite number of percentage error values in the multi-dimensional calibration space for the WIM device based, at least in part, on the determined at least two percentage error values, for the varied one of the distinct deviating factors. The operations further comprisesetting calibration coefficients for each point in the defined multi-dimensional calibration space for the WIM device based on the determined at least two percentage error values and the estimated other of requisite number of percentage error values.
According to still another exemplary aspect of the present disclosure, a method of measuring dynamic weight of a moving vehicle is disclosed. The method comprises determining appropriate calibration coefficient based on the distinct deviating factors of the moving vehicle; and applying the appropriate calibration coefficient in an equation:
W_corr=W_meas×(1-(E_d (s,w))/100),
wherein, ‘Wcorr’ is the corrected axle weight, ‘Wmeas’ is the measured axle weight, and ‘Ed(s,w)’ is the calibration coefficient for direction ‘d’, speed slice ‘s’ and weight slice ‘w’ in the multi-dimensional calibration space for the WIM device.
According to still another exemplary aspect of the present disclosure, a weigh-in-motion (WIM) device for measuring dynamic weight of a moving vehicle is disclosed. The controller in the WIM device is configured to determine appropriate calibration coefficient based on the distinct deviating factors of the moving vehicle; and apply the appropriate calibration coefficient in an equation:
W_corr=W_meas×(1-(E_d (s,w))/100),
wherein, ‘Wcorr’ is the corrected axle weight, ‘Wmeas’ is the measured axle weight, and ‘Ed(s,w)’ is the calibration coefficient for direction ‘d’, speed slice ‘s’ and weight slice ‘w’ in the multi-dimensional calibration space for the WIM device.
According to still another exemplary aspect of the present disclosure, a weigh-in-motion (WIM) apparatus incorporating the system and the WIM device as described above, is disclosed.
The foregoing summary is illustrative only and is not intended to be in any way limiting.In addition to the illustrative aspects, embodiments, and features described earlier, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE FIGURES
The accompanying drawings, which are incorporated herein and constitute a part of this disclosure, illustrate exemplary embodiments, and together with the description, serve to explain the disclosed principles.The same numbers are used throughout the figures to reference like features and components, wherein:
FIG. 1 depicts a block diagram of an exemplary arrangement for implementing a calibration process, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 2 depicts a schematic diagram of a system for calibrating a weigh-in-motion (WIM) device, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 3 depicts a schematic diagram of a computing system for system of FIG. 1, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 4 depicts a single weight slice, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 5 depicts a series of weight slices, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 6 depicts a visualization of a 3D calibration space, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 7 depicts a visualization of a 3D calibration space with discrete calibration points, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 8 depicts a visualization of a 3D calibration space with a surface plot of the discrete calibration points, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 9 depicts a visualization of a 3D calibration space with a weight slice, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 10 depicts a single speed slice, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 11 depicts a series of speed slices, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 12 depicts a visualization of a 3D calibration space with a speed slice, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 13 depicts a visualization of a 3D calibration space with a speed slice and a weight slice, in accordance with one or more exemplary embodiments of the present disclosure;
FIGS. 14-17 depict graphical user interfaces (GUI) for managing various operations of the present system, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 18 depicts an application of calibration coefficients to local scope, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 19 depicts an application of calibration coefficients to regional scope, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 20 depicts an application of calibration coefficients to global scope, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 21 depicts a GUI for managing one or more operations of the present system, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 22 depicts a visualization of distinctly saved multiple weight slices, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 23 depicts a visualization for 3D curve fitting of data of distinctly saved multiple weight slices, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 24 depicts a GUI for managing one or more operations of the present system, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 25 depicts local scope extension for a 3D calibration session, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 26 depicts regional scope extension for a 3D calibration session, in accordance with one or more exemplary embodiments of the present disclosure;
FIG. 27 depicts global scope extension for a 3D calibration session, in accordance with one or more exemplary embodiments of the present disclosure; and
FIG. 28 depicts a GUI for managing one or more operations of the present system, in accordance with one or more exemplary embodiments of the present disclosure.

DETAILED DESCRIPTION
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure.It will be apparent, however, to one skilled in the art that these specific details are only exemplary and not intended to be limiting.Additionally, it may be noted that the systems and/or methods are shown in block diagram form only in order to avoid obscuring the present disclosure.It is to be understood that various omissions and substitutions of equivalents may be made as circumstances may suggest or render expedient to cover various applications or implementations without departing from the spirit or the scope of the present disclosure.Further, it is to be understood that the phraseology and terminology employed herein are for the purpose of clarity of the description and should not be regarded as limiting.
Furthermore, in the present description, references to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure.The appearance of the phrase “in one embodiment” in various places in the specification is not necessarily referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.Further, the terms “a” and “an” used herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item.Moreover, various features are described which may be exhibited by some embodiments and not by others.Similarly, various requirements are described, which may be requirements for some embodiments but not for other embodiments.
The following description presents exemplary system and method for calibrating a Weigh-In-Motion (WIM) device configured for determining dynamic weight of a moving vehicle. The present system and method expand calibration space to a multiple dimension of “speed” – “axle weight” – “direction” of the moving vehicle versus error. The resulting calibration space of multiple points is determined by predictive interpolation and extrapolation of a small subset of the calibration space. The proposed architecture and associated algorithms are such that a graphical approach to adjustment of the calibration factors eliminates complex hand calculations. The present system and method greatly reduce the time required for calibration of the WIM device by making the calibration procedure process oriented. The present system and method support multiple sessions of calibration lending itself to overcome practical limitation in the site.The present system and method further supports graphical identification and manual manipulation of individual calibration coefficient leading to a progressively accurate calibrated system. In addition, the present system and method provides that calibration is secured by means of a security layer which implements a device generated random one-time password that is sent to the mobile number of the registered users. The present system and method can be configured to be administered by the Legal Metrology department thus making it tamper proof. The present system and method further provides that notification is sent to predefined authority if any of the set calibration coefficients for the WIM device is manipulated. The notification of unlocking the calibration is also sent to the local legal metrology officer by SMS and stored in cloud database.
In some implementations, the present disclosure may be embodied as a system, method, apparatus, or computer program product.Accordingly, in some implementations, the present disclosure may take the form of an entirely hardware implementation, an entirely software implementation (including firmware, resident software, micro-code, etc.) or an implementation combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, in some implementations, the present disclosure may take the form of a computer program product on a computer-usable storage medium having computer-usable program code embodied in the medium.
In some implementations, any suitable computer usable or computer readable medium (or media) may be utilized.The computer readable medium may be a computer readable signal medium or a computer readable storage medium.The computer-usable, or computer-readable, storage medium (including a storage device associated with a computing device or client electronic device) may be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or any suitable combination of the foregoing.More specific examples (a non-exhaustive list) of the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fibre, a portable compact disc read-only memory (CD-ROM), an optical storage device, a digital versatile disk (DVD), a static random access memory (SRAM), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, a media such as those supporting the internet or an intranet, or a magnetic storage device.Note that the computer-usable or computer-readable medium could even be a suitable medium upon which the program is stored, scanned, compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.In the context of the present disclosure, a computer-usable or computer-readable, storage medium may be any tangible medium that can contain or store a program for use by or in connection with the instruction execution system, apparatus, or device.
In some implementations, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave.In some implementations, such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof.In some implementations, the computer readable program code may be transmitted using any appropriate medium, including but not limited to the internet, wireline, optical fibre cable, RF, etc.In some implementations, a computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
In some implementations, computer program code for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java®, LabVIEW, myOpenLab, PyLab_Works, Smalltalk, C++ or the like.Java and all Java-based trademarks and logos are trademarks or registered trademarks of Oracle and/or its affiliates.However, the computer program code for carrying out operations of the present disclosure may also be written in conventional procedural programming languages, such as the "C" programming language, PASCAL, or similar programming languages, as well as in scripting languages such as JavaScript, PERL, or Python.The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer or server.In the latter scenario, the remote computer may be connected to the user’s computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the internet using an Internet Service Provider).In some implementations, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGAs) or other hardware accelerators, micro-controller units (MCUs), or programmable logic arrays (PLAs) may execute the computer readable program instructions/code by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.
In some implementations, the flowchart and/or block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus (systems), methods and computer program products according to various implementations of the present disclosure.Each block in the flowchart and/or block diagrams, and combinations of blocks in the flowchart and/or block diagrams, may represent a module, segment, or portion of code, which comprises one or more executable computer program instructions for implementing the specified logical function(s)/act(s).These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program instructions, which may execute via the processor of the computer or other programmable data processing apparatus, create the ability to implement one or more of the functions/acts specified in the flowchart and/or block diagram block or blocks or combinations thereof.It should be noted that, in some implementations, the functions noted in the block(s) may occur out of the order noted in the figures.For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
In some implementations, these computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks or combinations thereof.
In some implementations, the computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed (not necessarily in a particular order) on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts (not necessarily in a particular order) specified in the flowchart and/or block diagram block or blocks or combinations thereof.
Referring now to the example implementation of FIG. 1, there is shown calibration process 10 that may reside on and may be executed by a computer (e.g., computer 12), which may be connected to a network (e.g., network 14) (e.g., the internet or a local area network).Examples of computer 12 (and/or one or more of the client electronic devices noted below) may include, but are not limited to, a personal computer(s), a laptop computer(s), mobile computing device(s), a server computer, a series of server computers, a mainframe computer(s), or a computing cloud(s).In some implementations, each of the aforementioned may be generally described as a computing device.In certain implementations, a computing device may be a physical or virtual device.In many implementations, a computing device may be any device capable of performing operations, such as a dedicated processor, a portion of a processor, a virtual processor, a portion of a virtual processor, portion of a virtual device, or a virtual device.In some implementations, a processor may be a physical processor or a virtual processor.In some implementations, a virtual processor may correspond to one or more parts of one or more physical processors.In some implementations, the instructions/logic may be distributed and executed across one or more processors, virtual or physical, to execute the instructions/logic.Computer 12 may execute an operating system, for example, but not limited to, Microsoft® Windows®; Mac® OS X®; Red Hat® Linux®, or a custom operating system.(Microsoft and Windows are registered trademarks of Microsoft Corporation in the United States, other countries or both; Mac and OS X are registered trademarks of Apple Inc.in the United States, other countries or both; Red Hat is a registered trademark of Red Hat Corporation in the United States, other countries or both; and Linux is a registered trademark of Linus Torvalds in the United States, other countries or both).
In some implementations, the instruction sets and subroutines of the calibration process 10, which may be stored on storage device, such as storage device 16, coupled to computer 12, may be executed by one or more processors (not shown) and one or more memory architectures included within computer 12.In some implementations, storage device 16 may include but is not limited to: a hard disk drive; a flash drive, a tape drive; an optical drive; a RAID array (or other array); a random access memory (RAM); and a read-only memory (ROM).
In some implementations, network 14 may be connected to one or more secondary networks (e.g., network 18), examples of which may include but are not limited to: a local area network; a wide area network; or an intranet, for example.
In some implementations, computer 12 may include a data store, such as a database (e.g., relational database, object-oriented database, triplestore database, etc.) and may be located within any suitable memory location, such as storage device 16 coupled to computer 12.In some implementations, data, metadata, information, etc. described throughout the present disclosure may be stored in the data store.In some implementations, computer 12 may utilize any known database management system such as, but not limited to, DB2, in order to provide multi-user access to one or more databases, such as the above noted relational database.In some implementations, the data store may also be a custom database, such as, for example, a flat file database or an XML database.In some implementations, any other form(s) of a data storage structure and/or organization may also be used.In some implementations, calibration process 10 may be a component of the data store, a standalone application that interfaces with the above noted data store and/or an applet / application that is accessed via client applications 22, 24, 26, 28.In some implementations, the above noted data store may be, in whole or in part, distributed in a cloud computing topology.In this way, computer 12 and storage device 16 may refer to multiple devices, which may also be distributed throughout the network.
In some implementations, calibration process 10 may be accessed via one or more of client applications 22, 24, 26, 28.In some implementations, calibration process 10 may be a standalone application, or may be an applet / application / script / extension that may interact with and/or be executed within a collaboration application 20, a component of collaboration application 20, and/or one or more of client applications 22, 24, 26, 28.In some implementations, collaboration application 20 may be a standalone application, or may be an applet / application / script / extension that may interact with and/or be executed within calibration process 10, a component of calibration process 10, and/or one or more of client applications 22, 24, 26, 28.In some implementations, one or more of client applications 22, 24, 26, 28 may be a standalone application, or may be an applet / application / script / extension that may interact with and/or be executed within and/or be a component of calibration process 10 and/or collaboration application 20.Examples of client applications 22, 24, 26, 28 may include, but are not limited to, a standard and/or mobile web browser, an email application (e.g., an email client application), a textual and/or a graphical user interface, a customized web browser, a plugin, an Application Programming Interface (API), or a custom application.The instruction sets and subroutines of client applications 22, 24, 26, 28, which may be stored on storage devices 30, 32, 34, 36, coupled to client electronic devices 38, 40, 42, 44, may be executed by one or more processors and one or more memory architectures incorporated into client electronic devices 38, 40, 42, 44.
In some implementations, one or more of storage devices 30, 32, 34, 36, may include but are not limited to: hard disk drives; flash drives, tape drives; optical drives; RAID arrays; random access memories (RAM); and read-only memories (ROM).Examples of client electronic devices 38, 40, 42, 44 (and/or computer 12) may include, but are not limited to, a personal computer (e.g., client electronic device 38), a laptop computer (e.g., client electronic device 40), a smart/data-enabled, cellular phone (e.g., client electronic device 42), a notebook computer (e.g., client electronic device 44), a tablet (not shown), a server (not shown), a television (not shown), a smart television (not shown), a media (e.g., video, photo, etc.) capturing device (not shown), and a dedicated network device (not shown).Client electronic devices 38, 40, 42, 44 may each execute an operating system, examples of which may include but are not limited to, Android™, Apple® iOS®, Mac® OS X®; Red Hat® Linux®, or a custom operating system.
In some implementations, one or more of client applications 22, 24, 26, 28 may be configured to effectuate some or all of the functionality of calibration process 10 (and vice versa).Accordingly, in some implementations, calibration process 10 may be a purely server-side application, a purely client-side application, or a hybrid server-side / client-side application that is cooperatively executed by one or more of client applications 22, 24, 26, 28 and/or calibration process 10.
In some implementations, one or more of client applications 22, 24, 26, 28 may be configured to effectuate some or all of the functionality of collaboration application 20 (and vice versa).Accordingly, in some implementations, collaboration application 20 may be a purely server-side application, a purely client-side application, or a hybrid server-side / client-side application that is cooperatively executed by one or more of client applications 22, 24, 26, 28 and/or collaboration application 20.As one or more of client applications 22, 24, 26, 28, calibration process 10, and collaboration application 20, taken singly or in any combination, may effectuate some or all of the same functionality, any description of effectuating such functionality via one or more of client applications 22, 24, 26, 28, calibration process 10, collaboration application 20, or combination thereof, and any described interaction(s) between one or more of client applications 22, 24, 26, 28, calibration process 10, collaboration application 20, or combination thereof to effectuate such functionality, should be taken as an example only and not to limit the scope of the disclosure.
In some implementations, one or more of users 46, 48, 50, 52 may access computer 12 and calibration process 10 (e.g., using one or more of client electronic devices 38, 40, 42, 44) directly through network 14 or through secondary network 18.Further, computer 12 may be connected to network 14 through secondary network 18, as illustrated with phantom link line 54.Calibration process 10 may include one or more user interfaces, such as browsers and textual or graphical user interfaces, through which users 46, 48, 50, 52 may access calibration process 10.
In some implementations, the various client electronic devices may be directly or indirectly coupled to network 14 (or network 18).For example, client electronic device 38 is shown directly coupled to network 14 via a hardwired network connection.Further, client electronic device 44 is shown directly coupled to network 18 via a hardwired network connection.Client electronic device 40 is shown wirelessly coupled to network 14 via wireless communication channel 56 established between client electronic device 40 and wireless access point (i.e., WAP) 58, which is shown directly coupled to network 14.WAP 58 may be, for example, an IEEE 802.11a, 802.11b, 802.11g, Wi-Fi®, RFID, and/or Bluetooth™ (including Bluetooth™ Low Energy) device that is capable of establishing wireless communication channel 56 between client electronic device 40 and WAP 58.Client electronic device 42 is shown wirelessly coupled to network 14 via wireless communication channel 60 established between client electronic device 42 and cellular network / bridge 62, which is shown directly coupled to network 14.
In some implementations, some or all of the IEEE 802.11x specifications may use Ethernet protocol and carrier sense multiple access with collision avoidance (i.e., CSMA/CA) for path sharing.The various 802.11x specifications may use phase-shift keying (i.e., PSK) modulation or complementary code keying (i.e., CCK) modulation, for example.Bluetooth™ (including Bluetooth™ Low Energy) is a telecommunications industry specification that allows, e.g., mobile phones, computers, smart phones, and other electronic devices to be interconnected using a short-range wireless connection.Other forms of interconnection (e.g., Near Field Communication (NFC)) may also be used.
As will be discussed below, the calibration process 10 may at least help, e.g., improve existing technological processes associated with, e.g., querying of multiple data sources necessarily rooted in computer technology.
It will be appreciated that the computer processes described throughout are not considered to be well-understood, routine, and conventional functions.
Referring now to FIG. 2, an exemplary schematic diagram of a system (designated by the numeral 200) for calibrating a WIM device (such as, a WIM device 210 as depicted in FIG. 2) is illustrated, in accordance with one or more embodiments of the present disclosure. Herein, the WIM device 210 is configured for determining dynamic weight of a moving vehicle (such as, a vehicle 220 as depicted in FIG. 2). In particular, the WIM device 210 is configured to determine the axle weight of the vehicle 220. Hereinafter, the term “axle weight” has been interchangeably used simply with the term “weight” in some situations. The system 200 is shown to include the WIM device 210 with a weighing surface 212, which although shown separately from the WIM device 210 may, in some examples, be integral thereto without any limitations. It may be appreciated that the vehicle 220 may be made to stand (while static) and/or pass over (while moving) the weighing surface 212 for determine static and/or dynamic weight thereof.
Further, in the present embodiments, the WIM device 210 includes a controller (designated by the numeral 214) embedded in the WIM device 210 (herein, schematically shown outside of the WIM device 210 for illustration purposes). The controller 214 is adapted to store calibration coefficients for the WIM device 210. The controller is further configured to generate digital signals corresponding to measured instantaneous readings at the weighing surface 212 of the WIM device 210. In the present examples, the WIM device 210 may have a front-end signal conditioning, digitizing unit, peripheral devices and a simple display device.The controller 214 manages all the functionality of the WIM device 210. This also includes processing the raw low-level signal from analogy-to-digital convertor (ADC) in the weighing surface 212 and using a proprietary algorithm estimates the equivalent static weight.The controller 214 is also responsible for compensating known errors using the information derived from the calibration procedure.
The controller 214 may generally be implemented as a combination of a processor (not shown) and a memory (not shown) operatively coupled with each other.Herein, the memory may be capable of storing machine executable instructions, and the processor may be capable of executing the stored machine executable instructions for performing tasks related to calibration process 10.Examples of the memory include, but are not limited to, volatile memory devices (e.g., registers, cache, RAM) and/or non-volatile memory devices (e.g., ROM, EEPROM, flash memory, etc.).The processor may be embodied as one or more of various processing devices, such as a multi-core processor, a single core processor, a coprocessor, a microprocessor, a controller, a digital signal processor (DSP), a processing circuitry with or without an accompanying DSP, or various other processing devices including integrated circuits such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a microcontroller unit (MCU), a hardware accelerator, a special-purpose computer chip, or the like.Moreover, the processor may be a distributed or a unified system, without any limitations.
Further, as illustrated, the system 200 includes a computing device (designated by the numeral 230). Referring also to the example implementation of FIG. 3, there is shown a schematic depiction of the computing device 230.While computing device 230 is shown in this figure, this is for example purposes only and is not intended to be a limitation of this disclosure, as other configurations are possible.Additionally, any computing device capable of executing, in whole or in part, calibration process may be substituted for computing device 230 (in whole or in part) within FIG. 3, examples of which may include but are not limited to computer 12 and/or one or more of client electronic devices 38, 40, 42, 44. According to embodiments of the present disclosure, the computing device 230 is in communication with the controller 214 to receive generated digital signals corresponding to measured instantaneous readings by the WIM device 210 and adapted to have access to change the stored calibration coefficients in the controller 214, as required.
In some implementations, the computing device 230 may include a processor and/or microprocessor (e.g., microprocessor 300) configured to, e.g., process data and execute the above-noted code / instruction sets and subroutines.Microprocessor 300 may be coupled via a storage adaptor (not shown) to the above-noted storage device(s) (e.g., storage device 30).An I/O controller (e.g., I/O controller 302) may be configured to couple microprocessor 300 with various devices, such as keyboard 306, pointing/selecting device (e.g., touchpad, touchscreen, mouse 308, etc.), custom device (e.g., device 315), USB ports (not shown), and printer ports (not shown).A display adaptor (e.g., display adaptor 310) may be configured to couple display 312 (e.g., touchscreen monitor(s), plasma, CRT, or LCD monitor(s), etc.) with microprocessor 300, while network controller/adaptor 314 (e.g., an Ethernet adaptor) may be configured to couple microprocessor 300 to the above-noted network 14 (e.g., the Internet or a local area network).
The use of the computing device 230 is limited to initial setup of the WIM device 210, or specifically for calibration purposes of the WIM device 210.Therefore, it may not be necessary to retain the connection between the WIM device 210 and the computing device 230 after setup and calibration. The WIM device 210 and the computing device 230 could communicate using any of the standard protocols such as serial, TCP, Wi-Fi, Bluetooth etc.Due to the much higher capability of the computing device 230 in comparison to the WIM device 210, some of the higher order functionality is implemented in the computer program of the computing device 230. Since the computing device 230 usually has a display device (such as, the display 312), the user interacts with it, this piece of program is called the Graphical User Interface (GUI).Various calibration routines, co-efficient interpolation and extrapolation algorithms, graphical manipulation of the weight and speed slices, translation of the 3D graphical calibration space to equivalent representation in the indicator are some of the calibration function implemented in the GUI, as discussed in detail later in the description.
The system 200 of the present disclosure define a multi-dimensional calibration space for the WIM device 210. All the distinct factors that cause the accuracy of the system 200 to deviate from the ideal for which an independent correction is attempted constitute the calibration space. The factor that has the greatest influence on the accuracy of the WIM device 210 is the error on account of speed of the vehicle. This is caused due to limitations in the response of the combination of load sensing device and mechanical structure, dynamics of the axle suspension or lateral forces between the moving object in contact with the weighing surface. Herein, the multi-dimensional calibration space implements percentage error values versus at least one of distinct deviating factors affecting accuracy of the WIM device 210. In one or more examples, the at least one of distinct deviating factors comprises speed of the moving vehicle, axle weight of the moving vehicle and direction of travel of the moving vehicle.
In the present embodiments, correction for the error gives rise to the speed-error calibration sub space.This involves the system 200 to determine at least two percentage error values in the multi-dimensional calibration space for the WIM device 210 by moving a test vehicle thereon while varying one of the distinct deviating factors and measuring instantaneous readings thereof, and comparing the measured instantaneous readings with corresponding pre-determined readings of the respective one of the distinct deviating factors for the test vehicle. Further, the present system 200 is configured to estimate other of requisite number of percentage error values in the multi-dimensional calibration space for the WIM device 210 based, at least in part, on the determined at least two percentage error values, for the varied one of the distinct deviating factors.
A typical error graph (designated by the numeral 400) is illustrated in FIG. 4 which represents a single speed slice plot. Herein, the primary variable, speed is plotted along the X-axis while the dependent variable, error, is along the Y-axis. Thereby, a 2-Dimensional (2D) plot(such as, shown in the graph 400) is generated. Here the speed space is ranging from 0 to 20Km/Hr with an interval size of 0.5Km/Hr. Hence there are 41 points. Also, the nature of the graph suggests that there is little or no error till around 5Km/Hr beyond which there is a linear drop in error with speed. It may be contemplated that this type of response is very common across a wide variety of systems. Generally, the response of systems has been studied to follow standard mathematical models of that are described being of 1st or 2nd or higher order.
As discussed, the general operation of the WIM device 210 entails extraction of the equivalent static weigh across a multitude of speed weigh combinations.Hence restricting the calibration space to just speed versus percentage error (hereinafter, sometimes simply referred to as “error”) would mean overlooking the influence that different axle weights might have on the error graph 400 of FIG. 4.The present system 200 addresses this limitation by accommodating a separate speed-error curve for each different weight range, as illustrated in FIG. 5 which depicts multiple weight slices for different speeds.In FIG. 5, three different axle weights W1, W2 and W3 have their associated distinct speed-error correction curves 502, 504 and 506 respectively.Since the curves 502, 504 and 506 are each associated with a weight, they are referred to as weight-slices. It may be understood that it would be sufficient to have a single weight slice represent the characteristics of a range of weights in its vicinity. In practice too, the speed-error characteristics are not expected to vary much for a range of a few percent of the full scale weighing range. A 4% range for a 25 Tonne full scale range translates to 1000kgs. Thus, there would be a total of 25 weight slices to cover for the entire weighing range. Hence, for example, each weight slice may represent 500Kgs above and below its designated weight.That is, a 1000Kg weight slice would represent axle weights from 500Kg to 1499 Kg, for example.
It may be appreciated by a person skilled in the art based on the description of FIGS.4-5 that all the 25 weight slices each with 41 distinct factors for their associated speeds can be collectively represented in a 3-Dimensional space 600 as shown in FIG. 6. In FIG. 6, the two primary variables, speed and weight are associated with the X and Y axis respectively, while the percentage error (Err %) is associated with the Z axis.Thereby, the calibration space is extended to the 3rd dimension. Such 3D calibration space 600 has a total of 1025 individual correction factors.It may be appreciated that viewing them collectively conveys a complete picture of the error characteristics and the interplay between the two primary factors (speed and weight) on the error of the system 200. FIG. 7 illustrates one such representation of a 3D calibration space (designated by the numeral 700) with points plotted therein.As may be seen from the representation of FIG. 7, that in addition to the error varying with speed as described earlier, the error also has a dependency on the weight. For example, it can be seen that there is no error till around 5000Kg. As the axle weight increases, the error increases.The combined effect of higher weight and higher speed causes the graph to dip towards the lower front left corner. Though the calibration points look discrete, their applicability is continuous. FIG. 8 illustrates a 3D calibration space (designated by the numeral 800) to visualize continuous graph formed thereby. Further, FIG. 9 illustrates a 3D calibration space (designated by the numeral 900, similar to the 3D calibration space 800) with a weight slice (referred by the numeral 902). As shown in FIG. 9, the weight slice 902 can be identified in the 3D calibration space 900 and obtained by slicing the 3D calibration space 900 along desired weight value in the XZ plane.
In some examples, it may be convenient to view the 3D calibration space 800, 900with a different perspective. For example, instead of considering the 3D calibration space 900to be made up of different weight slices (such as, the weight slice 902) each with a corresponding speed-error graph, it may be understood that the 3D calibration space 900 is made up of different speed slices.In this case, each speed slice would contain a weight versus error graph. FIG. 10 depicts a speed slice 1000 as may be obtained from the 3D calibration space 800, 900. Also, it may be understood that series of speed slices would make up the same 3D calibration space 800, 900. FIG. 11 illustrates a series of speed slices for different weights.In FIG. 11, three different speeds S1, S2 and S3 have their associated distinct weight-error correction curves 1102, 1104 and 1106 respectively.Since the curves 1102, 1104 and 1106 are each associated with a speed, they are referred to as speed-slices. It may be understood that it would be sufficient to have a single speed slice represent the characteristics of a range of speeds in its vicinity. Also it may be understood that treatment of the 3D calibration space in this manner does not alter the individual calibration points.However, it may be useful to look at it in this perspective due to likely scenarios either during calibration or in the application domain of the dynamic weighing.An example would be while calibrating a WIM device for a rail in motion application where a reference train with different wagons of varying weights are used.A single run of this reference train at a particular speed would result in the determination of weight-error characteristics of that speed slice.FIG. 12 illustrates a 3D calibration space 1200 with a speed slice 1202 (at approx. 13 Km/hr). Further, FIG. 13 illustrates the 3D calibration space 1300 depicting an interrelation between a speed slice 1302 and a weight slice 1304 therein.
Thereby, the system 200 is configured to calculate and set calibration coefficients (also sometimes referred to as “correction factors”, “error coefficients” or simply “coefficients” without any limitations) for each point in the defined multi-dimensional calibration space for the WIM device 210 based on the determined at least two percentage error values and the estimated other of requisite number of percentage error values.It may be contemplated by a person skilled in the art that the calibration space can be further extended to the next higher dimension to account for errors due to travel direction. The 4D calibration space would have two individual 3D calibration spaces for each direction of travel. The next higher dimension of the calibration space could be towards accounting for residual errors due to the axle spacing. It could also be towards temperature compensation. The accuracy of the WIM device increases progressively with increase in dimension of the calibration space; however, the calibration complexity may also increase many times.
In particular, the present system 200 provides a graded approach to calibration. Herein, calibration of the WIM device 210 is carried out in the field. In view of this as well as the practical constrains imposed because of availability of time and resources, it may be useful to carry out the calibration in multiple sessions with increasing complexity.This is particularly true in case the entire 3D calibration coefficients need to be determined. By using this approach, all the 3D (a total of 1025 points) can be determined over a period of multiple 2D calibration sessions spanning across different time periods using different types of vehicles of different axle loads. In the interim, till all the coefficients are determined, they can be either left at their default values or they can be estimated by a process of interpolation and/or extrapolation.
FIG. 14 depicts a snapshot of a GUI 1400 that bundles all related functions of calibration grouped in a tab. To standardize and eliminate the complexity of manually calculating the calibration coefficients, the GUI 1400 embeds the setup, procedure and the computation in a software program. This not only abstracts the calibration process but also provides a visual and structured framework for the entire procedure.The left side space partitioned by a vertical line in the GUI 1400 includes the outputs of the various calibration steps carried out using the controls and inputs in the right side. The final output of the calibration procedure i.e. the 3D plot, the static gain and the system offset coefficients are contained in the ‘3D GRAPH’ sub tab. Various tags associated with the calibration coefficient like the calibration date, conditions and counter are also listed in this subtab. Associated tasks related to the handling of the calibration coefficients are also to be found here.Other intermediary outputs, procedures for manipulating, interpolating or extrapolating coefficients is located in the ‘GRAPHS’ sub tab. The right-side partition in the GUI 1400 shows the various calibration routines imbibed in the software framework. As will be explained later, this framework provides a structured approach to the calibration procedure. It is also setup to enable calibration in a graded fashion progressing from 0D calibration all the way to 4D calibration (as discussed later).The GUI 1400 allows enough flexibility to allow the user to adapt to different scenarios, while constraining the user to follow the methodology prescribed.
In the present embodiments, the system 200 is configured determine a zero-dimensional (0D) calibration coefficient by measuring instantaneous reading of the WIM device 210 with no load thereon. For example, as soon as the WIM device 210 is connected to sensor in the weighing surface 212 (platform or piezo sensor), the first correction is the single point correction. This is also referred to as dead load correction or offset correction.This corrects for the error in the system 200 under no load conditions.Usually after this calibration the system 200 is ready for measurement.Since this calibration is carried out without the need for any reference weight, and in keeping with the concept of multi-dimensional calibration, it is referred to as ‘0D’ calibration. The calibration coefficient E0D is calculated as shown below:
E_0D= E_(?0D?_temp )=W_0+E_(?0D?_temp )
Herein, ‘W0’ is the reading displayed with no load. Sometimes it may be necessary to repeat the procedure multiple times during the same session to reduce the error to acceptable limits.This is possible by copying the value of ‘E0D’ to ‘E0Dtemp’ till it is committed to the WIM device 210. It is to be noted that the above procedure is implemented in the GUI 1400 and only the final value is transferred to the WIM device 210 for final correction.The user has to only click the ‘Offset’ button in the GUI 1400 repeatedly till the user is satisfied with the residual value displayed next to the control.Once satisfied, the values are transferred to the controller 214 of the WIM device 210 by clicking on the ‘Set Calibration’ button.
The system 200 is further configured to determine a one-dimensional (1D) calibration coefficient by statically applying one or more known weights to the WIM device and correlating corresponding readings thereof. That is, the first level (0D) of calibration is followed up with a quick check of accuracy. In this stage of calibration, either a known reference weight is placed on the weighing surface 212 or a vehicle of known total mass is passed over at a typical speed.All associated inputs and controls for carrying out this procedure is contained in the ‘1D’ sub tab of the GUI 1400. Since the calibration is carried out with reference to only one influencing parameter (static weight), it is called as single point calibration, or as ‘1D’ calibration. In some implementations, such calibration is also commonly called as gain calibration.The calibration weight Wcal is entered into the GUI 1400. Herein, if the individual axle weights are not known, the weight of the entire vehicle or wagon can be entered along with the number of axles.In case the calibration axle(s) are preceded by axles of unknown weight(s), the same can be entered in the ‘Pre-Axle’ information tab.This arrangement if the GUI 1400 offers flexibility in adapting the procedure to suit to individual situations. For example, it is quite common in rail in motion (RIM) applications for the engine and a dummy buffer wagon to precede a calibration wagon.In case 1D calibration is to be performed in dynamic mode, it may be understood that the system 200 would need this additional information to exclude these axles from the computation of the correction factor. Thereafter, depending upon the mode, either each axle is individually placed on the weighing surface or the vehicle/wagon is passed over at a typical operating speed to obtain the measured weight ‘Wmeas’. The appropriate mode may be selected based on the situation or practicality of the selected method.
As discussed, 1D calibration can be either carried out in static mode or in dynamic mode. In both cases, only the weight information is used to determine the calibration coefficient. The coefficient is calculated as below:
E_1D=E_(?1D?_temp )=W_cal/W_meas ×E_(?1D?_temp )
This computation is triggered by the user by pressing the ‘Calc. Gain’ button in the GUI 1400. The procedure can be iterated repeatedly till the desired level of accuracy is achieved. The computed calibration coefficient is stored in the variable ‘E1Dtemp’ till committed to the controller 214 of the WIM device 210 as explained earlier. The 0D and 1D coefficients can be identified in the GUI 1400 with their associated names, herein ‘Sys. Offset’ and ‘Static Gain’ respectively. In addition, these coefficients can also be manually entered by clicking on the ‘pencil-shaped icon’ present next to the ‘Static Gain’ value.
It may be understood that both the 0D and 1D calibration coefficients are used in the WIM device 210 to achieve correction to the final output. Correction is achieved by applying these values to each individual sample of the ADC as shown in the equation below.
W_(inst.)=W_ADC×E_1D+E_0D
Here each individual sample of the ADC, ‘WADC’ is adjusted to determine the instantaneous weight value ‘Winst’ of the WIM device 210.
It may be appreciated that as a vehicle with multiple axles moves over the WIM device 210, the measured instantaneous weight values provide a waveform. The axle weight is determined from a continuous analysis of the series of instantaneous values by identifying portions of the waveform that is relevant to the computation of the axle weight and axle speed, and also estimates the axle to axle spacing. This information is useful for the next level of calibrations, i.e. two dimensional (2D) calibration and three dimensional (3D) calibration. The correction for axle weight variations due to speed and or axle weight is achieved by using the correction factor indexed by the weight and speed from the 3D calibration space (such as, the 3D calibration space 700 of FIG. 7).The process of determining the calibration coefficients is by way of either 2D or 3D calibration, as discussed in the proceeding paragraphs. The framework for 2D calibration is grouped together in the ‘2D’ sub tab of the GUI 1400. Since the 3D calibration space can be thought of being equivalent to either a series of weight or speed slices, 2D calibration can be either of speed or weight.
In the 2D speed calibration, as discussed earlier, weight slice provides error with respect to speed for a particular range of weights. Hence, a 2D speed calibration would have a 2D graph of error versus speed for a specific weight slice.Such representation is shown in GUI 1500 of FIG. 15.Here, a 2D speed calibration is being initiated with a 2-axle vehicle of total mass 12000Kgs. This information needs to be specified in the ‘2D Calibration Input’ table. The GUI 1500 provides two possible setups. In first setup, the information is entered without regard to individual axle weights.The system interprets this as a setup for a speed calibration for a weigh slice of 6000Kgs. While it may seem inappropriate, this is acceptable if it is the first time a 2D calibration is being attempted. This is because the variation in the computed axle weight is generally dominated by speed much more that due to weight.Also, if the influence of weight is unclear, it is acceptable to initially assume no such influence exists. Additionally, determining individual axle weights is not straightforward on a static scale. If such variations do exist, they will be revealed in future calibration sessions and can then be corrected. Hence in such a scenario, the system computes the error coefficients for the weight slice by using the total vehicle weight. This is usually followed by extrapolating the error related to this weight slice for all the weight slices in the memory. In the alternate setup, shown in dashed box in FIG. 15, the individual axle weights may be individually entered as shown. In this case, a tick symbol next the axle number indicates to the system that the information of the particular axle needs to be considered in the error computation. Hence in this case, the error is assigned to the weight slice of 8000 Kgs.Since this is a setup for 2D calibration, only one axle can be selected.This variation of the setup is useful if individual axle weights can be determined. Also, this setup is useful if only a particular weight slice is to be separately calibrated. This could be because individual slice calibrations are planned for future sessions, or if the need to adjust just this slice was found to be necessary. Usually after the process of determining the coefficients of the weight slice, they are applied to just the corresponding weight slice. However, the option to apply to other weigh slices is also possible, but at user discretion.
As explained earlier, each weight slice has 41 error coefficients, each corresponding to a distinct speed. The process of determining these coefficients is initiated by multiple runs of the vehicles at different speeds. Each run will result in the computation of the error coefficient ‘Ei’ as follows:
E_i (s,w)=((W_(?meas?_i ) (s))/((1-(E_d (s,w))/100) )-W_cal)/(W_(?meas?_i ) (s) ) × 100
Where, ‘s’ is the speed between zero to maximum speed (in Km/hr), ‘Ed(s,w)’ is the current indexed error coefficient for the corresponding speed (s)and weight slice (w) for direction (d), ‘Wmeasi(s)’ is the measured axle weight at speed (s) for the ith run of the session, and ‘Wcal’ is the selected axle weight specified in the table divided by number of axles.
After each run, the vehicle is reversed and the process is continued till there are sufficient number of data points. It is also possible to reject the reading and continue with the calibration process, if the need arises. It is to be noted that the readings in the reverse direction is not considered by the system for computing the error coefficients.
In the present embodiments, the information related to the 3D calibration coefficients can be stored in the controller 214 of the WIM device 210 as a 3-dimensional array. The embedded program in the weighing indicator can apply the corresponding correction factor by first indexing by weight and then by speed. Also, it may be appreciated that the representation of the 4D calibration space can be stored in the controller 214 of the WIM device 210 by way of two 3 dimensional arrays. Such storage techniques may be contemplated by a person skilled in the art and thus have not been discussed in detail herein for the brevity of the present disclosure.
Generally, it is not practical to determine all the error coefficients of the weight slice by taking measurements at each individual speed.As discussed, the present system estimates other of requisite number of percentage error values in the multi-dimensional calibration space for the WIM device based, at least in part, on the determined at least two percentage error values, for the varied one of the distinct deviating factors. In one or more examples, estimating other of requisite number of percentage error values utilizes one or more of predictive interpolation and extrapolation techniques. In the present embodiments, estimating other of requisite number of percentage error values utilizes curve fitting technique. Using the technique of curve fitting analysis, a set of curve parameters or coefficients could be obtained from the data set to obtain a functional description that describes the curve over the desired space.
Of the many methods of fitting a curve, the present system 200 preferably employs the least squares technique for obtaining a good fit. This technique works by minimizing the sum of the squares of the offsets ("the residuals") of the points from the curve. A linear regression fits a straight line to a set of paired observations.However, in a general sense, a curve better represents measurement data. The least squares method can be extended to fit the data to higher order polynomials. Of the numerous methods two such are General Linear Fit and General Polynomial Fit. Alternatively, the Least Absolute Residual and Bi-square fitting techniques can also be implemented which are more robust fitting techniques than the Least square in terms of their sensitivity to outliers.
In the General Linear Least squares technique, the curve is represented by a linear combination of linear fit coefficients and basis functions of the general form shown below:
y_i=f_ij (a,x)=?_(j=0)^(k-1)¦a_j f_ij (x)=a_0 f_i0 (x)+a_1 f_i1 (x)+?+a_(k-1) f_(ik-1) (x) ;i=0,1,….,n-1
Where, a = {a0, a1, a2, …, an – 1} are the linear fit coefficients, ‘k’ is the total number of functions, ‘fij(x)’ are modal functions and ‘n’ is the total number of observations.
Herein, the general LS linear fit model is a multiple linear regression model. As may be understood, a multiple linear regression model uses several variables, fi0(x), fi1(x), fi2(x),…, fik-1(x), to predict one variable, ‘yi’. Hence, given a set of observations and a general idea of the relationship between ‘x’ and ‘y’, it is possible to determine the set of coefficients ‘a’ that best represent the set of observations.
In the general Polynomial Fit technique, the data is represented by a polynomial function of the general form shown below:
y_i=f_ij (a,x)=?_(j=0)^m¦a_j x_i^j=a_0+a_1 x_i+a_2 x_i^2+?+a_m x_i^m
By using the curve fitting technique, an optimal data set as well as a set of coefficients is obtained that is the best fit to the given relation in the presence of outliers in the measurements. Using the General Linear Fit or General Polynomial Fit coefficients, the values of the error at additional points in the required range can be estimated by substituting into the formula. Alternately, using a technique of linear interpolation which is a method of curve fitting using linear polynomials, new data points can be constructed within the range of a discrete set of desired data points. By using this methodology, the complete speed slice can be computed with reasonable certainty starting with a fraction of the total points. In some cases, the noise or repeatability associated with the measurement is also factored in, which leads to a better assessment of the best fit curve.
In an embodiment, the system 200 of the present disclosure further allowsmanual manipulation of calibration coefficients. FIG. 16 depicts a GUI 1600 for graphical manipulation of calibration coefficients. Herein, if required, the curve can be edited by clicking on the ‘Edit Slice’ icon.Editing is accomplished by clicking on a point in the graph at the desired point. Using this method, the graphs can be corrected to be closer to the expected value. Other functions such as ‘Clear Slice’ and ‘Save Slice’ are also seen in the GUI 1600. The ‘Clear Slice’ function is used to delete the manual edits and the ‘Save Slice’ function is used to commit to memory, of the controller 214 in the WIM device 210, the manual edits of the slice before the next operation.
FIG. 17 provides a GUI 1700 depicting a setup for 2D Weight calibration. In 2D Weight calibration, the error variation due to axle weight is sought to be corrected. As mentioned earlier, there are 25 discrete weight slices or weight bins each with a distinct error coefficient. To get a reasonable estimate of all the coefficients, the same technique described earlier can be used. However, a minimum number of distinct weights are required. It is beneficial to have them as spread out as possible to get a better spread of the measurement points.
For a truck in motion applications, there will be practical difficulties to arrange a single vehicle with this many axles of different weights.However, a series of vehicles with different axle weights can be used instead. Care should be taken to ensure that all these vehicles cross the weighing surface at near about the same speed. The cross symbol adjacent to a particular axle number indicates that the readings corresponding to the axle are not to be considered for the calibration procedure. It is also possible to specify the combined axle group weight as in the case of vehicles with tandem axles.The results of the calibration procedure are applied to the speed slice corresponding to the measured speed of the vehicle over the weighing surface 212.
For a rail in motion application, it is easier to arrange a test train with a wide distribution of axle weights. For instance, an example assembled test train may consist of carriages C1 to Cn having weights W1 to Wn respectively drawn by a locomotive L1. In such a case, it may be worthwhile to consider doing speed as well as weight calibration in a single session, which would be the case in 3D calibration (as described later). However, 2D weight calibration can still be useful if during subsequent verification, the need to adjust the readings across all weight slices at a particular speed is found to be necessary.
For the above listed reasons, 2D weight calibration may not be as widely usable as 2D speed calibration.The 2D calibration procedure is initiated by pressing on the ‘Start’ button in the GUI 1700. The process is completed once all the axles are detected. The resulting estimated curve for the associated speed slice is also shown in FIG. 17.It is to be noted that the error graph is a speed slice that has the error with respect to weight for a given speed of 5 Km/hr. As before, the calibration coefficients in the particular weight slice can be estimated starting with a measurement of a smaller fraction of points.
The present system 200 further allows for expanding the error coefficients to fill the calibration space. As described above, 2D calibration results in the determination of the error coefficients for a single speed or weight slice. The extension of the measured error coefficients to the rest of the calibration space is a consideration that is based on the current stage of the calibration, overall calibration planning as well as user judgement. The scope of the extension is either ‘Local Scope’, ‘Regional Scope’ or ‘Global Scope’.The selection of the scope is by the ‘Select Scope’ button as shown in the GUI 1700 of FIG. 17. In Local scope, the extrapolated coefficients labelled as ‘New’ in FIG. 16 are disregarded and only the ‘Measured’ coefficients displayed by the black square markerare updated. The remaining coefficients are retained to their ‘Current’ value as displayed by line formed by points labelled as ‘Current’.FIG. 18 depicts a resultant 3D calibration space 1800 in which a plane 1802 is a marker for the 6000Kg weight slice. Selection of the ‘Regional scope’ updates the entire extrapolated data of the slices in the memory. These could be either weight or speed slices. A 2D calibration session allows only a single slice in the memory. Selection of the ‘Regional scope’ will only update the slice loaded in the memory. Specifically, all other slices will retain their current coefficients.This also means that multiple slices, each as an outcome of a previous calibration session can be stored distinctly in the calibration space. FIG. 19 provides a 3D calibration space 1900 resulting from extension of the calibration coefficients to ‘Regional Scope’. In one or more examples, ‘Select Scope’ control is disabled till there is at least a single slice in the memory. This serves as an interlock preventing the user to write an empty slice into the calibration space.
Further, selecting the ‘Global Scope’ results in a 3D curve fitting based on the available curves in the memory. If there is a single curve in the memory, then basically the same curve is copied to the remaining slices. The extension of the calibration coefficients of FIG. 16 of the 6000Kg weight slice to the ‘Global Scope’ will result in the 3D calibration space 2000 to appear, as depicted in FIG. 20. Hence after a 2D calibration session, selection of this scope should be done if there is no variation expected in the other dimension. It could also be that these set of error coefficients result in a lesser error than the default values and the next calibration session is not planned in the near future. Once the choice of the scope is made, the appropriate translation into the 3D space can be done by clicking the ‘Generate Calibration’ button in the GUI 1700 of FIG. 17. As before, the final set of the error coefficients can be copied in to the controller 214 of the WIM device 210 by using the ‘Set Calibration’ button located in the 3D Graphs sub tab in the GUI 1400 of FIG. 14.
It may be appreciated that the above examples demonstrate the expansion of the calibration coefficients starting with a weight slice. The same can be achieved by starting with a single speed slice, as shown in FIG. 17 without departing from the scope and the spirit of the present disclosure.
FIG. 21 depicts a GUI 2100 for 3D calibration.It is similar to the setup of 2D calibration. Here 3D calibration is carried out as simultaneous 2D speed calibrations for multiple weight slices.Hence at a minimum 2 distinct weights need to be entered and selected. Also as pointed out earlier, calibration is only by way of taking multiple reading at different speeds (‘Speed 3D’).The other notable difference is that there is an option to ‘Load All Slices’. This allows the user to review the entire 3D calibration spaces as multiple weight slices, as shown. It may be appreciated that 3D calibration can be achieved either in multiple sessions or a single session.
In a multi-session calibration, several 2D session are planned and the results saved distinctly. These can be of either 2D speed or weigh calibration. Once a set of 2D slices have been independently determined and saved distinctly in the calibration space, the 3D calibration space can be reasonably estimated by extending the technique of curve fitting to 3D spaces. Since this process is in the 3D space, the controls for this is located in the ‘3D’ sub tab. In case, previously different 2D speed calibration sessions had been conducted. At the end of each session, the results were extended to ‘Regional Scope’.FIG. 22 depicts resultant 3D Calibration space 2200 therefrom. In this example, 5 different weight slices have been individually determined and saved distinctly. The remaining weight slices have retained their default values. Now, with the previously estimated regional slices, the global space can be reasonable estimated.
The procedure for this process begins by navigating to the ‘3D’ tab and then by clicking on ‘Load All Slices’. This deconstructs the 3D calibration space stored in the indicator to individual weight slices and displays them on speed versus error graphs. Reviewing each slice can reveal the slices that had undergone a calibration procedure. These sliced can then be loaded into the memory by clicking on the ‘Edit Slice’ followed by ‘Save Slice’.If required, the slices can be adjusted for corrections if any. Thereafter, selecting the ‘Global Scope’ followed by ‘Generate Calibration’ will result in the estimation of the remaining calibration scope. FIG. 23 depicts resultant calibration space 2300 therefrom. Operationally, 3D curve fitting is accomplished by translating the data contained in different speed slices to points in the weight slice. This is followed by either a general linear or polynomial fit as described earlier, but instead for the speed slices.
In a single-session calibration, the estimation of the calibration coefficients for the entire 3D space can be accomplished in a single session. This is practically easy to do so in rail in motion application where a test train is available.In this configuration, the locomotive is followed by a minimum of 5 calibration wagons of different axle weights.Optionally a buffer wagon immediately follows the locomotive. The guard wagon is also usually present at the end of the rake. The entire calibration space consisting of 1025 distinct speed-weight-error points can be determined by 15 -20 runs of the test train at different speeds. At the end of each run, as in the case of 2D calibration, the option to consider/reject the measurement or continue/stop the procedure is available. This process can be completed in a couple of hours. Re-verification and finer adjustments may require a similar time. All the graphical manipulation options that were described earlier as part of 2D calibration procedure are also available for each of slice for which data is being obtained based on the weight distribution of the test train. FIG. 24 depicts a GUI 2400 as an input setup for the single session 3D calibration using a test train. In this example the test train consists of a locomotive followed by 6 carriages. The weights of the locomotive and the last carriage are excluded by the cross mark. It is not necessary to enter their exact weights. However, the axle count should be specified correctly. The weight of the rest of the carriages are individually considered in the calibration procedure as indicated by a tick mark next to the row containing their information. The weight slices that are simultaneously determined are also indicated in FIG. 24. At the end of the measurement process, the same options for extending the scope of the calibration are available. Again, based on the situation and user judgement, the appropriate choice can be made to achieve the desired results. Herein, FIG. 25 depicts local scope extension for a 3D calibration session, FIG. 26 depicts regional scope extension for a 3D calibration session, and FIG. 27 depicts global scope extension for a 3D calibration session.
In some cases, four dimensional (4D) calibration is necessitated due to deficiencies in the weighing surface, consisting of the area on either side of the weighing platform. Based on the approach direction, variations in the measured axle weigh are observed.The 4D calibration process is basically a repeat of 0D to 3D procedure after selection of the direction using either the ‘WIM Dir’ button or an auto detection process. However, many a times, there would be only minor changes required on top of the previously determined coefficients for the 3D space for the other directions. Hence a good starting point would be to start by copying the 3D coefficients from the other direction. This can be achieved by the ‘Backup Calibration’ and ‘Restore Calibration’ features.The calibration can be backed up in to a file in a computer and restored again to the other direction and saved back to the indicator. There after minor corrections can be accomplished by manual edits, local or regional scopes of extension.
In the present disclosure, the correction to the axle weight is achieved by first indexing the appropriate correction factor based on the direction, measured speed and measured axle weight. The direction can be either a manual entry, ‘WIM Dir’ or auto detected based on inputs from sensors such as loop detectors, IR sensors or similar. The following relation is used to correct for weight variation.
W_corr=W_meas×(1-(E_d (s,w))/100)
Where, ‘Wcorr’ is the corrected axle weight, ‘Wmeas’ is the measured axle weight, and ‘Ed(s,w)’ is the calibration (error) coefficient for direction ‘d’, speed slice ‘s’ and weight slice ‘w’.
In an embodiment of the present disclosure, the system 200 is further configured to provide a security layer to limit access for manipulating the set calibration coefficients for the WIM device 210. The WIM device 210 is required to have security features that control how users can interact and alter the main functional characteristic of the system, i.e. the measurement accuracy. The resources that can affect the measurement are the hardware components, the settings of the parameters that have a bearing on the scaling of the final result and the calibration controls. The security of the present system 200 is architected around principles of physical access control and separation of duties. This in conjunction with process of identification, authentication and authorization which makes it a multi-step authentication which is a form of layered security where it is unlikely that all layers would be compromised by someone using only one type of attack.
In the present embodiments, dismantling of the box of the WIM device 210 can be prevented by passing the sealing wire through the holes of the sealing nuts provided on the rear side to the device. A lead seal is put at the end of the wire for stamping purpose. This prevents access to the internal hardware. In addition, there is a two-position switch which enables or inhibits the changes to sealable configuration parameters. The user can be prevented from calibrating the device by moving the position of the slide switch to ‘Calibration lock’ position before putting the cover plate. This plate can be sealed by passing a sealing wire.Once so secured, the calibration controls in the software are disabled preventing unauthorized calibration as show in the figure.
Further, the present system 200 limits software access to prevent manipulation, since configuration and calibration of the hardware is only possible from the software. The software communicates using the designated port and exchanges encoded authentication information, prior to enabling any interaction with the hardware. In case an approved hardware device is not detected, the system 200 can change the GUI to hence show the cursor as ‘busy’ or the like.
The system 200 of the present disclosure supports three identities i.e. ‘User’, ’Engineer’ and ‘Administrator’. Each of these identities has a different authentication password and is associated with different levels of privileges. The default identity of the ‘User’ has only the basic privilege of viewing the system status and health, and viewing of calibration information or changing any setting is not allowed. Authenticating with the ‘Engineer’ identity enables viewing of the calibration information as well as changing some parameters that have no bearing on the weight accuracy of the system. Finally, all privileges including calibration and parameters that might have a bearing with the weight accuracy rest with the ‘Administrator’identity. However, the weight related functions cannot be modified without due authorization from the Legal Metrology Department.
FIG. 28 depicts a GUI 2800 for providing a security layer to limit access for manipulating the set calibration coefficients for the WIM device 210. In the operation of the present system 200, once the DIP switch is in the ‘Calibration locked’ position and sealed, the WIM device cannot be calibrated without first moving the switch to the ‘Unlocked’ position. This requires the physical seal to be cut. Typically, this is done after informing the Legal Metrology Officer. Once unlocked, if the authenticated identity with the required privilege (Administrator) intends to perform calibration on a machine he/she shall enter his/her mobile number. The keyed mobile number is checked in the database on a web server against mobile numbers of engineers who are assigned and authorized to calibrate the machine. This database is a cloud database that can be managed either by the OEM or the Legal Metrology Department. The database supports various user groups. For example, the list of officers from the Legal Metrology Department can be in a different group to whom the SMS is sent by default. If the entered mobile number is found in the database, then SMS will be sent to the matched number in addition to numbers in the OEM and Legal Metrology groups. If the user is not validated, then GET OTP key and Validate Key will not be enabled, thus preventing manipulation. Once the user mobile is validated, the user shall press the GET OTP key. The random OTP is generated in the indicator and sent to the user’s mobile phone using the OEM server. The user should then enter the OTP that is received on his/her mobile phone in the “Validate OTP” box which pops-up after the user presses the Validate Key.The user entered OTP is subsequently validated in the WIM device 210. On Successful validation, the system 200 shall allow the user to proceed with calibration of the WID device 210.If the OTP entered is invalid or is timed out (e.g., more than 30 min), the calibration is not allowed.
It may be appreciated that the computing device (such as, the computing device 230) that is connected to the WIM device 210 being calibrated is required to be connected to the internet. The host PC can communicate to another electronic device connected on the World Wide Web using a web service that is a technology that allows for making connection. SMS gateway providers facilitate SMS traffic between machines and mobile subscribers. Using the HTTP (Hypertext Transfer Protocol) application programming interface (APIs), SMS functionality can be added to any program. The API defines a set of functions using which the developers can submit requests and receive responses. Of the various architectures used for implementing web services, two are REST and JSON. REST has proved to be a popular choice for implementing Web Services due to their simplicity. JSON uses name/value pairs and hence is easy to understand. Using methods like HTTP POST, Web Request data can be submitted to a Web page. The POST request method requests that the web server accept the data enclosed in the body of the request message. Several functions or commands are defined as part of the API that help integrate various aspects of the SMS service into the program. For example, one such command could be to check is a particular number exists in the databases associated with the web service.Using the syntax defined in the API and a command, the number specified in the body can be checked for a match. The response that is sent back to the computing device contains the status as well as other details associated with the number. By parsing the status of the command, the ‘Get OTP’ button can be enabled for further generation of the One Time Password in the WIM device 210. The OTP is generated in the WIM device 210 using a complex mathematical algorithm, such as hash chain or Linear Feedback Shift Registers, to generate a random one-time password. Each password is not guessable, even when previous passwords are known. Pressing the ‘Get OTP’ causes the OTP to be generated in the indicator and sent to the PC. The PC then places a request to the Web server using the command ‘send’ along with the mobile numbers to which the SMS has to be sent. It also constructs the message that contains the OTP as well as the Geo-location of the instruments. The geo location can be determined by an API that can be integrated into the program. In an embodiment, the system 200 is further configured to send a notification to predefined authority if any of the set calibration coefficients for the WIM device 210 is manipulated. The response contains the status indicating if the delivery was successful. In addition to the SMS being sent to the designated officer of the Legal Metrology department, the web server stores a record of all SMSs that were sent using the web services. This make the entire process very difficult to tamper and subvert.
The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description.They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching.The exemplary embodiment was chosen and described in order to best explain the principles of the present disclosure and its practical application, to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated.