Description:DESCRIPTION
Technical Field
This disclosure relates generally to methods and systems for monitoring the health of components of a vehicle, and more specifically, to a system and a method for determining wear information and remaining useful life of a clutch in the vehicle.
BACKGROUND
Monitoring the lifespan of a clutch stands as a pivotal factor in ensuring its optimal functionality and predicting potential failures. This proactive approach to identifying issues before they escalate is integral in maintaining the health of a clutch system and the vehicle. Predictive failure analysis, a technique used to assess the condition of a clutch, serves as the keystone for anticipating maintenance needs, thereby minimizing downtime. The concept revolves around preventively gauging the state of the clutch to forecast when maintenance will be necessary. This foresight allows for scheduling corrective measures, such as maintenance or repairs, which can effectively prevent unforeseen clutch failures.
Traditionally, monitoring the condition of clutches involved labor-intensive methods. Manual inspections and tests were conducted, encompassing the observation of numerous operational parameters. Parameters ranged from engine speed and ambient temperature to various mechanical states, including clutch and brake pressure statuses. However, this manual monitoring process was not only exhaustive but also periodic, requiring frequent interruptions to assess the clutch's health and prevent sudden breakdowns. Regrettably, the downside of these conventional techniques was their tendency to disrupt the continuous operation of the clutch and hence the vehicle. Moreover, relying on manual intervention for monitoring introduced the possibility of errors. The manual inspection process not only incurred higher chances of inaccuracies but also contributed to increased operational costs. As clutches gradually wear out, they exhibit symptoms such as poor gear shifting, reduced mileage, and an increase in the effort required to engage the clutch pedal. These symptoms, if not detected and addressed promptly, lead to performance degradation and potentially costly repairs.
Therefore, there is a requirement of a methodology to effectively determine wear information and remaining useful life of a clutch of a vehicle.
SUMMARY OF THE INVENTION
In one embodiment, a method of determining clutch wear information and a remaining useful life of a clutch of a vehicle is disclosed. The method may include receiving, by a controller, one or more operational parameters with respect to the clutch of the vehicle. In an embodiment, the one or more operational parameters may be extracted from vehicle information received from the vehicle for a plurality of time instants. The method may further include determining, by the controller, vehicle weight information based on a first set of the one or more operational parameters using a weight determination model. The method may further include, determining, by the controller, clutch slip information based on a second set of the one or more operational parameters using a clutch slip determination model. The method may further include, determining, by the controller, clutch temperature information based on a third set of the one or more operational parameters, the vehicle weight information, and the clutch slip information using a clutch temperature determination model. In an embodiment, the clutch temperature determination model may be a pre-trained machine learning model based on domain knowledge with respect to the clutch. The method may further include, determining, by the controller, the wear information based on the vehicle weight information, the clutch slip information, and the clutch temperature information using a clutch wear determination model. In an embodiment, the clutch wear determination model may be a pre-trained multi-dimensional multi-variable machine learning model based on domain knowledge with respect to the clutch. The method may further include determining, by the controller, the remaining useful life of the clutch based on the wear information of the clutch. The method may further include providing, by the controller, at least one of the wear information and the remaining useful life of the clutch for rendering.
In another embodiment, a system of determining wear information and a remaining useful life of a clutch of a vehicle is disclosed. The system may include a controller and a memory coupled to the controller. The memory may store a set of instructions, which, on execution, may cause the controller to receive one or more operational parameters with respect to the clutch of the vehicle. In an embodiment, the one or more operational parameters may be extracted from vehicle information received from the vehicle for a plurality of time instants. The controller may further determine vehicle weight information based on a first set of the one or more operational parameters using a weight determination model. The controller may further determine clutch slip information based on a second set of the one or more operational parameters using a clutch slip determination model. The controller may further determine clutch temperature information based on a third set of the one or more operational parameters, the vehicle weight information, and the clutch slip information using a clutch temperature determination model. In an embodiment, the clutch temperature determination model may be a pre-trained machine learning model and may be based on domain knowledge with respect to the clutch. The controller may further determine wear information based on the vehicle weight information, the clutch slip information, and the clutch temperature information using a clutch wear determination model. In an embodiment, the clutch wear determination model may be a pre-trained multi-dimensional multi-variable machine learning model based on domain knowledge with respect to the clutch. The controller may further determine the remaining useful life of the clutch based on the wear information. The controller may further provide at least one of the wear information and the remaining useful life of the clutch for rendering.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles.
FIG. 1 illustrates a block diagram of a monitoring system for determining wear information and remaining useful life of a clutch in a vehicle, in accordance with an embodiment of the present disclosure.
FIG. 2 illustrates a functional block diagram of monitoring device, in accordance with an embodiment of the present disclosure.
FIG. 3 illustrates a graphical user interface (GUI) of a monitoring application, in accordance with an embodiment of the present disclosure.
FIG. 4 illustrates a flowchart of a method for determining wear information and a remaining useful life of a clutch of a vehicle, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE DRAWINGS
The foregoing description has broadly outlined the features and technical advantages of the present disclosure in order that the detailed description of the disclosure that follows may be better understood. Additional features and advantages of the disclosure will be described hereinafter which forms the subject of the claims of the disclosure. It should be appreciated by those skilled in the art that the conception and specific embodiments disclosed may be readily utilized as a basis for modifying other devices, systems, assemblies and mechanisms for carrying out the same purposes of the present disclosure. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the scope of the disclosure as set forth in the appended claims. The novel features which are believed to be characteristics of the disclosure, to its device or system, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present disclosure.
The terms “including”, “comprises”, “comprising”, “comprising of” or any other variations thereof, are intended to cover a non-exclusive inclusions, such that a system or a device that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.
Reference will now be made to the exemplary embodiments of the disclosure, as illustrated in the accompanying drawings. Wherever possible, same numerals have been used to refer to the same or like parts. The following paragraphs describe the present disclosure with reference to FIGs. 1 - 4. It is to be noted that the system may be employed in any vehicle including but is not limited to a passenger vehicle, a utility vehicle, a commercial vehicle, and any other transportable machinery. For a sake of clarity, vehicle is not shown.
Monitoring the lifespan of a clutch stands as a pivotal factor in ensuring its optimal functionality and predicting potential failures. This proactive approach to identifying issues before they escalate is integral in maintaining the health of a clutch system and the vehicle. Predictive failure analysis, a technique used to assess the condition of an object, serves as the keystone for anticipating maintenance needs, thereby minimizing downtime. Accordingly, the present disclosure provides a method and system for determining wear information and a remaining useful life of a clutch of a vehicle.
Referring now to FIG. 1, a block diagram of a monitoring system 100 for determining wear information and a remaining useful life of a clutch in a vehicle, in accordance with an embodiment of the present disclosure is illustrated. The monitoring system 100 may include a monitoring device 102, a telemetry control unit (TCU) 110, an external device 112, and a database 114, communicably coupled to each other through a wired or wireless communication network 108, for monitoring wear information and the remaining useful life of a clutch (not shown) of a vehicle (not shown).
In an embodiment, the TCU 110 may be implemented in the vehicle. In an embodiment, the TCU 110 may be used to gather and manage data related to vehicle’s performance, location, and operational status. The TCU 110 may be responsible for collecting information from various sensors placed throughout the vehicle and then transmitting this data to the monitoring device 102 via the network 108 for monitoring, analysis, etc.
In some embodiment, examples of the clutch in a vehicle may include, but is not limited to, a friction clutch, a single plate clutch, a multi-plate clutch, a centrifugal clutch, a hydraulic clutch, an electromagnetic clutch, etc. The monitoring device 102 may include a controller 104, a memory 106. In an embodiment, the controller 104 may be integrated with the TCU 110 of the vehicle.
By way of an example, the monitoring device 102 may be implemented as a computing device which may be a control unit of the automobile. In an embodiment, the control unit may include an Electronic Control Unit (ECU) disposed in the automobile. In an embodiment, the monitoring device 102 includes a processor 104 and a memory 106. In an embodiment, the functions of the processor 104 may interchangeably be performed by a controller (not shown). In an embodiment, examples of processor 104 may include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, Nvidia®, FortiSOC™ system-on-a-chip processors or other future processors. The memory 106 may store instructions that, when executed by the processor 104, cause the processor 104 to wear information and a remaining useful life of a clutch of a vehicle (not shown). The memory 106 may be a non-volatile memory or a volatile memory. Examples of non-volatile memory may include but are not limited to a flash memory, a Read Only Memory (ROM), a Programmable ROM (PROM), Erasable PROM (EPROM), and Electrically EPROM (EEPROM) memory. Examples of volatile memory may include but are not limited to Dynamic Random Access Memory (DRAM), and Static Random-Access memory (SRAM).
In an embodiment, the communication network 108 may be a wired or a wireless network or a combination thereof. The network 108 can be implemented as one of the different types of networks, such as but not limited to, ethernet IP network, intranet, local area network (LAN), wide area network (WAN), the internet, Wi-Fi, LTE network, CDMA network, 5G and the like. Further, network 210 can either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further network 108 can include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
In an embodiment, the TCU 110, the external device 112 and the database 114 may be communicably connected to the monitoring device 102 via vehicle communication bus, operating on wireless protocols, including, but not limited to A²B (Automotive Audio Bus), AFDX, ARINC 429, Byteflight, CAN (Controller Area Network), D2B – (Domestic Digital Bus), FlexRay, IDB-1394, IEBus, I²C, ISO 9141-1/-2, J1708 and J1587, J1850, J1939 and ISO 11783 – an adaptation of CAN for commercial (J1939) and agricultural (ISO 11783) vehicles, Keyword Protocol 2000 (KWP2000), LIN (Local Interconnect Network), MOST (Media Oriented Systems Transport), IEC 61375, SMARTwireX, SPI, and/or VAN – (Vehicle Area Network), and the like.
In an embodiment, the database 114 may be enabled in a cloud or a physical database and may store vehicle information received from the vehicle for each of the plurality of time instants through the TCU 110. In an embodiment, the vehicle information may include a vehicle speed, an ambient temperature, an engine RPM, a vehicle acceleration, gear information, odometer data, vehicle ignition state, a clutch press state, a brake press state, a position of an acceleration pedal, etc. In an embodiment, the database 114 may store data input by the external device 112 or output generated by the monitoring device 102.
In an embodiment, the monitoring device 102 may receive a request for determining wear information and remaining useful life of the clutch and the vehicle from the external device 112. In an embodiment, the monitoring device 102 and the external device 112 may be a computing system, including but not limited to, a smart phone, a laptop computer, a desktop computer, a notebook, a workstation, a portable computer, a handheld, a scanner, or a mobile device. In an embodiment, the monitoring device 102 may be, but not limited to, in-built into the external device 112 or may be a standalone computing device.
In an embodiment, the monitoring device 102 may perform various processing for determining wear information and remaining useful life of the clutch of the vehicle. By way of an example, the monitoring device 102 may receive one or more operational parameters with respect to the clutch of the vehicle. In an embodiment, the one or more operational parameters may be extracted from vehicle information received from the vehicle for a plurality of time instants.
The monitoring device 102 may further determine vehicle weight information based on a first set of the one or more operational parameters using a weight determination model. In an embodiment, the first set of the one or more operational parameters may include, but are not limited to, a break press state, a vehicle speed, an engine torque, an engine RPM, a vehicle acceleration, gear information, etc.
The monitoring device 102 may further determine clutch slip information based on a second set of the one or more operational parameters using a clutch slip determination model. In an embodiment, the second set of the one or more operational parameters may include, but is not limited to, a clutch press state, gear information, an engine RPM, a vehicle speed, etc. In an embodiment, in order to determine the clutch slip information, the clutch slip determination model may determine differential box RPM based on, but not limited to, a wheel speed and a final drive ratio. Further, based on the differential box RPM and a gear ratio the clutch slip determination model may determine a gear box RPM. Further, the clutch slip determination model may determine the clutch slip information based on a difference between the engine RPM and the gear box RPM.
The monitoring device 102 may further determine clutch temperature information based on a third set of the one or more operational parameters, the vehicle weight information, and the clutch slip information using a clutch temperature determination model. In an embodiment, the clutch temperature information may be a pre-trained machine learning model based on domain knowledge with respect to the clutch. In an embodiment, the third set of the one or more operational parameters may include, but are not limited to, a clutch press state, a vehicle speed, the clutch slip information, the vehicle weight information, etc. Further, the clutch temperature information may include final temperature post clutch release and final temperature before clutch press.
The monitoring device 102 may further determine wear information of the clutch based on the vehicle weight information, the clutch slip information, and the clutch temperature information using a clutch wear determination model. In an embodiment, the wear information may include a cumulative clutch wear. In an embodiment, the clutch wear determination model may be a pre-trained multi-dimensional multi-variable machine learning model and may be trained based on domain knowledge with respect to the clutch. The monitoring device 102 may further determine the remaining useful life of the clutch based on the wear information. In an embodiment, the remaining useful life of the clutch may be further determined based on initial thickness of a clutch plate and odometer data. In an embodiment, at least one of the wear information and the remaining useful life of the clutch may be rendered, via a graphical user interface (GUI) or a notification, on at least one of an information system of the vehicle or a user device. The monitoring device 102 may further provide at least one of the wear information and the remaining useful life of the clutch for rendering.
The monitoring device 102 may further transmit field data to a remote server (not shown). In an embodiment, the remote server may be enabled as a cloud server. In an embodiment, the field data may include the one or more operational parameters, the wear information and the remaining useful life of the clutch. The monitoring device 102 may further receive an updated clutch temperature determination model and an updated clutch wear determination model at a periodical interval from the remote server. In an embodiment, the updated clutch temperature determination model and the updated clutch wear determination model may be generated by the remote server based on the field data received from a plurality of controllers in a plurality of vehicles.
Referring now to FIG. 2, a functional block diagram of monitoring device, in accordance with an embodiment of the present disclosure is illustrated. The monitoring device 102 may include a parameter receiving module 202, a vehicle weight determination module 204, a clutch slip determination module 206, a clutch temperature determination module 208, clutch wear determination module 210, a clutch remaining useful life (RUL) determination module 212 and a model updating module 214.
The parameters receiving module 202 may receive one or more operational parameters with respect to the clutch of the vehicle. In an embodiment, the one or more operational parameters may be determined from vehicle information received from the vehicle for a plurality of time instants when the clutch of the vehicle is engaged or operated. In an embodiment, the vehicle information received from the vehicle for each of plurality of time instants may include, but is not limited to, a vehicle speed, an ambient temperature, an engine RPM, a vehicle acceleration, gear information, odometer data, vehicle ignition state, a clutch press state, a brake press state, a position of an acceleration pedal, etc. determined for the plurality of time instants.
The vehicle weight determination module 204 may determine vehicle weight information based on a first set of the one or more operational parameters using a weight determination model. In an embodiment, the vehicle weight determined may be real time weight of the vehicle during the runtime. In an embodiment, the real time weight of the vehicle may be dependent on the load of people or goods being carried in the vehicle. In an embodiment, the first set of the one or more operational parameters may include, but is not limited to, a break press state, a vehicle speed, an engine torque, an engine RPM, a vehicle acceleration, gear information, etc. determined for the plurality of time instants. In an exemplary embodiment, the vehicle weight information may be calculated based on formula given by equation (1) given below:
Fwheel\ =\frac{Tengine\ .\ Ntorque\ converter\ .\ Ntransmission\ .\ Ndifferential\ .Fmechanical\ loss}{Rtire} …… (1)
Wherein,
Tengine = engine torque,
Ntorque_convertor = torque convertor ratio,
Fmechanical_loss = mechanical loss,
Ndifferenetial = differential ratio,
Ntransmission = transmission ratio,
Rtire = rolling radius.
The clutch slip determination module 206 may determine clutch slip information based on a second set of the one or more operational parameters using a clutch slip determination model. In an embodiment, the clutch slip information may be slip that when the clutch may be pressed and released. Accordingly, clutch slippage may take place when a clutch fails to adequately engage or disengage the transmission (gear box), causing the vehicle to slip out of gear or to struggle to stay in gear when accelerating. In an embodiment, the clutch slippage may be caused when there is not enough friction in the clutch, so that engine speed rises without a corresponding increase in vehicle speed. In an embodiment, the second set of the one or more operational parameters may include, but is not limited to, a clutch press state, gear information, an engine RPM, a vehicle speed, etc. determined for the plurality of time instants. In an embodiment, in order to determine the clutch slip information, the clutch slip determination model may determine a differential box RPM based on a wheel speed and a final drive ratio. Further, the clutch slip determination model may determine a gear box RPM based on the differential box RPM and a gear ratio. Further, the clutch slip determination model may determine the clutch slip information based on a difference between the engine RPM and gear box RPM. In an exemplary embodiment, the clutch slip information may be calculated based on formula given by equation (2) given below:
Clutch\ Slip=\frac{(Input\ shaft\ speed-Engine\ Speed)}{Engine\ Speed} ……(2)
Wherein,
Input shaft speed = speed of the transmission shaft of the vehicle.
The clutch temperature determination module 208 may determine clutch temperature information based on a third set of the one or more operational parameters, the vehicle weight information, and the clutch slip information using a clutch temperature determination model. In an embodiment, the clutch temperature information is indicative of the temperature variation in the clutch during the clutch release event or clutch press event. In an embodiment, the temperature variations in the clutch may cause wear out of the clutch.
In an embodiment, the clutch temperature determination model may be a pre-defined machine learning model and may be trained based on domain knowledge with respect to the clutch. In an embodiment, the third set of the one or more operational parameters may include a clutch press state, a vehicle speed, the clutch slip information, the vehicle weight information, and the clutch information may include final temperature post clutch release and final temperature before clutch press. In an embodiment, the clutch temperature determination model may include a heat generation machine learning (ML) model and a cooling ML model. In an embodiment, the heat generation ML model may receive input such as, but not limited to, clutch slip during each instant when the clutch is in a pressed state and real-time weight of the vehicle. Further, the heat generation ML model may predict the final temperature of the clutch post clutch released after each clutch pressed state. Further, the cooling ML model may receive input such as, but not limited to, average speed of the vehicle and an ambient temperature of the clutch at time instants when the clutch is in released state. The cooling ML model may predict a temperature of the clutch that may be there just before the instant clutch may be pressed i.e. final temperature before clutch press. Accordingly, the based on the determination of the variations between the ground truth values and the predicted values of the clutch temperature in the released state and the pressed state. Accordingly, the clutch wear model module 210 may utilize the clutch temperature information to determine the wear information of the clutch.
The clutch wear determination module 210 may determine wear information of the clutch based on the vehicle weight information, the clutch slip information, and the clutch temperature information using a clutch wear determination model. In an embodiment, the wear information may include a cumulative clutch wear due to clutch slip information, clutch weight information and the clutch temperature information. In an embodiment, the clutch wear determination model may be a pre-trained multi-dimensional multi-variable machine learning model and may be based on domain knowledge with respect to the clutch. In an embodiment, the domain knowledge with respect to the clutch may include wear information of the clutch measured in millimeter for a plurality of distance traveled and clutch temperature previously determined for various vehicles during instants when the clutch was in pressed and release states. Accordingly, the clutch wear determination module 210 may predict a clutch wear level in millimeter using the clutch wear determination model.
In an embodiment, the clutch temperature model used by the clutch temperature determination module 208 and the clutch wear determination model used by the clutch wear determination module 210 may be regression based ML models. In an embodiment, the regression based ML models may have an accuracy of about, but not limited to, 98.1%. In an embodiment, the clutch temperature model and the clutch wear determination model may be training using training data comprising the third set of operational parameters and the domain knowledge with respect to the clutch from vehicle that may have run about 20,000 kms. Further, the clutch temperature model and the clutch wear determination model may be validated for using operational data of the plurality of operational parameters received from a vehicle that may have run for about 15,000 kms. Accordingly, the validated the clutch temperature model and the clutch wear determination model may have an accuracy of about 96.5 %.
The clutch RUL determination module 212 may determine remaining useful life of the clutch based on the wear information of the clutch. In an embodiment, the remaining useful life of the clutch may be further determined based on initial thickness of a clutch plate and odometer data. In an embodiment, the odometer data may include the overall distance covered by the vehicle in kilometers. In an exemplary embodiment, the remaining useful life calculated based on formula given by equation (3) given below:
Remaining\ Useful\ Life=\frac{D\ast(Initial\ Clutch\ Thickness-Wear\ Information)}{Wear\ Information} …….(3)
Wherein,
D = Overall distance covered by the vehicle (Km),
Initial clutch thickness = thickness of the clutch when manufactured
Wear information = wear information of the clutch determined by the clutch wear determination module 210.
In an embodiment, at least one of the wear information and the remaining useful life of the clutch may be rendered, via a graphical user interface (GUI) or a notification, on at least one of an information system of the vehicle or a user device.
Further, the model updating module 214 may transmit field data to a remote server. In an embodiment, the field data may include the one or more operational parameters, the wear information, and the remaining useful life of the clutch. The model updating module 214 may transmit the field data to a remote server (not shown) communicably coupled to the monitoring device 102. In an embodiment, the remote server (not shown) may be communicably connected to the monitoring device 102 of various other vehicle and may receive their corresponding field data. Accordingly, the field data captured by the remote server may be used to update the clutch temperature model and the clutch wear determination model. Accordingly, the model updating module 214 may receive an updated clutch temperature determination model and updated clutch wear determination model at a periodical interval from the remote server. In an embodiment, the updated clutch temperature determination model and the updated clutch wear determination model may be generated by the remote server based on the field data received from a plurality of controllers in a plurality of vehicles. Accordingly, the updated clutch temperature determination model and updated clutch wear may be used by the clutch temperature determination module 208 and the clutch wear determination module 210.
It should be noted that all such aforementioned modules 202-214 may be represented as a single module or a combination of different modules. Further, as will be appreciated by those skilled in the art, each of the modules 202-214 may reside, in whole or in parts, on one device or multiple devices in communication with each other. In some embodiments, each of the modules 202-214 may be implemented as dedicated hardware circuit comprising custom application-specific integrated (ASIC) or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. Each of the modules 202-214 may also be implemented in a programmable hardware device such as a field programmable gate array (FGPA), programmable array logic, programmable logic device, and so forth. Alternatively, each of the modules 202-214 may be implemented in software for execution by various types of processors (e.g. processor 104). An identified module of executable code may, for instance, include one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, function, or other construct. Nevertheless, the executables of an identified module or component need not to be physically located together but may include disparate instructions stored in different locations which, when joined logically together, include the module, and achieve the stated purpose of the module. Indeed, a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different applications, and across several memory devices.
As will be appreciated by one skilled in the art, a variety of processes may be employed for determining wear information and the remaining useful life of a clutch of a vehicle. For example, the exemplary monitoring system 100 and the associated monitoring device 102 may determine wear information and the remaining useful life of a clutch of a vehicle. In particular, as will be appreciated by those of ordinary skill in the art, control logic and/or automated routines for performing the techniques and steps described herein may be implemented by the monitoring system 100 and the associated monitoring device 102 either be hardware, software, or combinations of hardware and software. For examples, suitable code may be accessed and executed by the one or more processor and/or controllers on the monitoring system 100 to perform some or all of the techniques described herein. Similarly, application specific integrated circuits (ASICs) configured to perform some, or all of the controllers described herein may be included in the one or more controllers on the monitoring system 100.
Referring now to FIG. 3, a graphical user interface (GUI) 300 of a monitoring application, in accordance with an embodiment of the present disclosure is disclosed. In an embodiment, the one or more operational parameters are received from the monitoring device 102 and may be rendered via the GUI 300 of the monitoring application on the external device 112. In an embodiment, the GUI 300 is rendered based on installation of the monitoring application on the external device 112 upon coupling of the external device 112 with the monitoring device 102.
In an embodiment, the GUI 300 may include at least one user selectable option 302 to select two or more vehicle types from a list of plurality of vehicle types for a comparative analysis of the selected vehicle’s clutch’s performance. The GUI 300 may further include one selectable option 304 to select the vehicle for single vehicle’s descriptive analysis. The GUI 300 may further include one selectable option 306 to select a unique vehicle ID associated to each of the plurality of vehicle types for the single vehicle’s descriptive analysis.
The GUI 300 may further include a primary display area 308 to display a graphical representation of number of clutch press events corresponding to the selected two or more vehicle types in 302.
The GUI 300 may further include a secondary display area 310 to display a graphical representation of number of clutch wear rate for single vehicle’s descriptive analysis corresponding to one of the selected vehicle in 304 and 306.
The GUI 300 may further include a third display area 312 that may display a clutch wear rate for the selected two or more vehicle types in 302 and a comparative analysis of the clutch wear rate in between each of the selected two or more vehicle types in 302.
The GUI 300 may further include a fourth display area 314 to display an average clutch temperature during clutch press for the selected two or more vehicle types in 302 and a comparative analysis of the average clutch temperature during clutch press in between each of the selected two or more vehicle types.
Referring now to FIG. 4, a flowchart of a method 400 for determining wear information and a remaining useful life of a clutch of a vehicle, in accordance with an embodiment of the present disclosure is illustrated. In an embodiment, method 400 may include a plurality of steps that may be performed by the processor 104 to determine wear information and remaining useful life of a clutch of a vehicle.
FIG. 4 is explained in conjunction with FIGs. 1 and 2. Each step of the method 400 may be executed by various modules of the monitoring device 102.
At step 402, one or more operational parameters may be received with respect to the clutch of the vehicle. In an embodiment, the one or more operational parameters may be extracted from vehicle information received from the vehicle for a plurality of time instants.
Further at step 404, vehicle weight information may be determined based on a first set of the one or more operational parameters using a weight determination model. In an embodiment, the first set of the one or more operational parameters may include, but are not limited to, a break press state, a vehicle speed, an engine torque, an engine RPM, a vehicle acceleration, gear information, etc.
Further at step 406, clutch slip information may be determined based on a second set of the one or more operational parameters using a clutch slip determination model. In an embodiment, the second set of the one or more operational parameters may include, but are not limited to, a clutch press state, gear information, an engine RPM, a vehicle speed, etc. In an embodiment, in order to determine the clutch slip information, the clutch slip determination model may determine a differential box RPM based on a wheel speed and a final drive ratio. Further, the in order to determine the clutch slip information, the clutch slip determination model may determine a gear box RPM based on the differential box RPM and gear ratio. The clutch slip determination model may determine the clutch slip information based on a difference between the engine RPM and gear box RPM.
Further at step 408, clutch temperature information may be determined based on a third set of the one or more operational parameters, the vehicle weight information, and the clutch slip information using a clutch temperature determination model. In an embodiment, the clutch temperature determination model may be a pre-defined machine learning model and may be pre-trained based on domain knowledge with respect to the clutch. In an embodiment, the third set of the one or more operational parameters may include, but are not limited to, a clutch press state, a vehicle speed, the clutch slip information, the vehicle weight information, the clutch information, etc. The clutch information may include a final temperature post clutch release and a final temperature before clutch press.
Further at step 410, wear information of the clutch may be determined based on the vehicle weight information, the clutch slip information, and the clutch temperature information using a clutch wear determination model. In an embodiment, the wear information of the clutch may include cumulative clutch wear. In an embodiment, the clutch wear determination model may be a pre-trained multi-dimensional multi-variable machine learning model and may be pre-trained based on domain knowledge with respect to the clutch.
Further at step 412, remaining useful life of the clutch may be determined based on the wear information of the clutch. In an embodiment, the remaining useful life of the clutch may be further determined based on initial thickness of a clutch plate and odometer data. Further, in an embodiment, at least one of the wear information and the remaining useful life of the clutch may be rendered, via a graphical user interface (GUI) or a notification, on at least one of an information system of the vehicle or a user device.
Further at step 414, at least one of the wear information and the remaining useful life of the clutch may be provided for rendering.
Further at step 416, field data may be transmitted to a remote server. In an embodiment, the field data may include the one or more operational parameters, the wear information and the remaining useful life of the clutch received from a plurality of controllers in a plurality of vehicles.
Further at step 418, an updated clutch temperature determination model and updated clutch wear determination model may be received at a periodical interval from the remote server. In an embodiment, the updated clutch temperature determination model and the updated clutch wear determination model may be generated by the remote server based on the field data received from a plurality of controllers in a plurality of vehicles.
Thus, the disclosed method and system try to overcome the technical problem of determining wear information and the remaining useful life of a clutch of a vehicle.
As will be appreciated by those skilled in the art, the techniques described in the various embodiments discussed above are not routine, or conventional, or well-understood in the art. The techniques discussed above provide for determining wear information and the remaining useful life of a clutch of a vehicle.
As will be also appreciated, the above described techniques may take the form of computer or controller implemented processes and apparatuses for practicing those processes. The disclosure can also be embodied in the form of computer program code containing instructions embodied in tangible media, such as floppy diskettes, solid state drives, CD-ROMs, hard drives, or any other computer-readable storage medium, wherein, when the computer program code is loaded into and executed by a computer or controller, the computer becomes an apparatus for practicing the invention. The disclosure may also be embodied in the form of computer program code or signal, for example, whether stored in a storage medium, loaded into and/or executed by a computer or controller, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the invention. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
By way of the above, the techniques described in the various embodiments discussed above provide for determining wear information and the remaining useful life of a clutch of a vehicle. The techniques do away with the need for manual intervention in monitoring, and thereby eliminate the possibilities of manual errors. Further, the techniques provide for a monitoring process which is time efficient and cost efficient, as compared to the manually performed monitoring. Further, the techniques do not require dedicated wear and temperature sensors. Further, the techniques require minimum cloud storage due to temporary storage of vehicle data. Further, the techniques require minimum processing requirement since algorithm is executed for every vehicle after every particular interval. Further, the techniques provision of reset in-case customer changes the clutch set.
In light of the above-mentioned advantages and the technical advancements provided by the disclosed methods and systems, the claimed steps as discussed above are not routine, conventional, or well understood in the art, as claimed steps enable the following solutions to the existing problems in conventional technologies. Further, the claimed steps bring an improvement in the functioning of the device itself as the claimed steps provide a technical solution to a technical problem.
The specification has described a method and system for determining wear information and remaining useful life of a clutch of a vehicle. The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for the purpose of illustration, and not limitation. Further, the boundaries of the functioning building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relations thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to a person skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.
It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.” Ranges can be expressed herein as from “about” one particular value, and/or to “about” another particular value. When such a range is expressed, another aspect includes from the one particular value and/or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another aspect. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint.
In addition, where features or aspects of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.
While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.
, Claims:1. A method (400) of determining wear information and a remaining useful life of a clutch of a vehicle, the method comprising:
receiving (402), by a controller (104), one or more operational parameters with respect to the clutch of the vehicle,
wherein the one or more operational parameters are extracted from vehicle information received from the vehicle for a plurality of time instants;
determining (404), by the controller (104), vehicle weight information based on a first set of the one or more operational parameters using a weight determination model;
determining (406), by the controller (104), clutch slip information based on a second set of the one or more operational parameters using a clutch slip determination model;
determining (408), by the controller (104), clutch temperature information based on a third set of the one or more operational parameters, the vehicle weight information, and the clutch slip information using a clutch temperature determination model, wherein the clutch temperature determination model is a pre-trained machine learning model and is pre-trained based on domain knowledge with respect to the clutch;
determining (410), by the controller (104), the wear information based on the vehicle weight information, the clutch slip information, and the clutch temperature information using a clutch wear determination model, wherein the clutch wear determination model is a pre-trained multi-dimensional multi-variable machine learning model and is pre-trained based on domain knowledge with respect to the clutch;
determining (412), by the controller (104), the remaining useful life of the clutch based on the wear information; and
providing (414), by the controller (104), at least one of the wear information and the remaining useful life of the clutch for rendering.

2. The method (400) as claimed in claim 1, wherein at least one of the wear information and the remaining useful life of the clutch is rendered, via a graphical user interface (GUI) or a notification, on at least one of an information system of the vehicle or a user device.

3. The method (400) as claimed in claim 1, wherein the vehicle information received from the vehicle for each of the plurality of time instants comprises a vehicle speed, an ambient temperature, an engine RPM, a vehicle acceleration, gear information, odometer data, vehicle ignition state, a clutch press state, a brake press state, and a position of an acceleration pedal.

4. The method (400) as claimed in claim 1, wherein the first set of the one or more operational parameters comprises a brake press state, a vehicle speed, an engine torque, an engine RPM, a vehicle acceleration, and gear information.

5. The method (400) as claimed in claim 1, wherein the second set of the one or more operational parameters comprises a clutch press state, gear information, an engine RPM, a vehicle speed,
wherein the clutch slip determination model determines the clutch slip information by:
determining a differential box RPM based on a wheel speed and a final drive ratio,
determining a gear box RPM based on the differential box RPM and gear ratio, and
determining the clutch slip information based on a difference between the engine RPM and the gear box RPM.

6. The method (400) as claimed in claim 1, wherein the third set of the one or more operational parameters comprises a clutch press state, a vehicle speed, the clutch slip information, the vehicle weight information, and
wherein the clutch temperature information comprises final temperature post clutch release and final temperature before clutch press.

7. The method (400) as claimed in claim 1, wherein the wear information comprises a cumulative clutch wear.

8. The method (400) as claimed in claim 1, wherein the remaining useful life of the clutch is further determined based on initial thickness of a clutch plate and odometer data.

9. The method (400) as claimed in claim 1, further comprising transmitting (416), by the controller (104), field data to a remote server, wherein the field data comprises the one or more operational parameters, the wear information and the remaining useful life of the clutch.

10. The method (400) as claimed in claim 9, comprising:
receiving (418), by the controller (104), an updated clutch temperature determination model and an updated clutch wear determination model at a periodical interval from the remote server, wherein the updated clutch temperature determination model and the updated clutch wear determination model is generated by the remote server based on the field data received from a plurality of controllers in a plurality of vehicles.

11. A system (100) for determining wear information and a remaining useful life of a clutch of a vehicle, comprising:
a processor (104); and
a memory (106) communicably coupled to the processor (104), wherein the memory stores processor-executable instructions, which, on execution, cause the processor (104) to:
receive one or more operational parameters with respect to the clutch of the vehicle,
wherein the one or more operational parameters are extracted from vehicle information received from the vehicle for a plurality of time instants;
determine vehicle weight information based on a first set of the one or more operational parameters using a weight determination model;
determine clutch slip information based on a second set of the one or more operational parameters using a clutch slip determination model;
determine clutch temperature information based on a third set of the one or more operational parameters, the vehicle weight information, and the clutch slip information using a clutch temperature determination model,
wherein the clutch temperature determination model is a pre-trained machine learning model based on domain knowledge with respect to the clutch;
determine wear information based on the vehicle weight information, the clutch slip information, and the clutch temperature information using a clutch wear determination model, wherein the clutch wear determination model is a pre-trained multi-dimensional multi-variable machine learning model and is pre-trained based on domain knowledge with respect to the clutch;
determine remaining useful life of the clutch based on the wear information; and
provide at least one of the wear information and the remaining useful life of the clutch for rendering.

12. The system (100) as claimed in claim 11, wherein at least one of the wear information and the remaining useful life of the clutch is rendered, via a graphical user interface (GUI) or a notification, on at least one of an information system of the vehicle or a user device.

13. The system (100) as claimed in claim 11, wherein the vehicle information received from the vehicle for each of the plurality of time instants comprises a vehicle speed, an ambient temperature, an engine RPM, a vehicle acceleration, gear information, odometer data, vehicle ignition state, a clutch press state, a brake press state, and a position of an acceleration pedal.

14. The system (100) as claimed in claim 11, wherein the first set of the one or more operational parameters comprises a brake press state, a vehicle speed, an engine torque, an engine RPM, a vehicle acceleration, and gear information.

15. The system (100) as claimed in claim 11, wherein the second set of the one or more operational parameters comprises a clutch press state, gear information, an engine RPM, a vehicle speed,
wherein the clutch slip determination model determines the clutch slip information by:
determining differential box RMP based on wheel speed and final drive ratio, determining gear box RPM based on the differential box RPM and gear ration, and determining the clutch slip information based on a difference between the engine RPM and gear box RPM.

16. The system (100) as claimed in claim 11, wherein the third set of the one or more operational parameters comprises a clutch press state, a vehicle speed, the clutch slip information, the vehicle weight information, and wherein the clutch temperature information comprises final temperature post clutch release and final temperature before clutch press.

17. The system (100) as claimed in claim 11, wherein the wear information comprises cumulative clutch wear.

18. The system (100) as claimed in claim 11, wherein the remaining useful life of the clutch is further determined based on initial thickness of a clutch plate and odometer data.

19. The system (100) as claimed in claim 11, wherein the controller-executable instructions, which, on execution, cause the processor (104) to:
transmit field data to a remote server, wherein the field data comprises the one or more operational parameters, the wear information and the remaining useful life of the clutch.

20. The system (100) as claimed in claim 19, wherein the controller-executable instructions, which, on execution, cause the processor (104) to:
receive an updated clutch temperature determination model and updated clutch wear determination model at a periodical interval from the remote server, wherein the updated clutch temperature determination model and the updated clutch wear determination model is generated by the remote server based on the field data received from a plurality of controllers in a plurality of vehicles.