DESC:FIELD
The present disclosure relates to the field of control systems.
BACKGROUND
A control system plays an important role in the operation of any process plant. Any failure in the control system may lead to the shut-down of the process plant or damage to the personnel and equipment installed therein, or production losses. Every control system has a limited life span which can be quantified in terms of hours of operation. The life span or healthiness of every control system is dependent on a number of operational and environmental factors. Environmental factors such as excess vibrations, noise from external sources exceeding the rated limit for equipment’s, dust and gasses deposits on the electronic and machine components, temperature and humidity, are also major contributors in control system failures.
Further, the life span of every control system is affected by the failure of its components, structural failures in the system, and manufacturing defects in the system. In the long run, such environmental factors and localized failures can lead to an overall failure of the control system, which further affects the operation, productivity, and efficiency of the entire plant. Moreover, the failure of the control system also degrades the instruments connected in the plant. For example, in a power plant, frequent tripping of turbine or boiler can reduce the life of the components of the turbine, and in-turn damage the turbine itself.
In the conventional control systems, to ensure that the control system operates without any interruption and to ensure its maximum availability, the redundancy of controllers is preferred, which allows duplication of critical parts of controllers, usually in the form of a backup or fail-safe. However, in many cases, such redundancy changeover from one controller to another, may not complete successfully, due to failure of some parts of the controller during the changeover itself. Due to unsuccessful redundancy changeover, the plant operations may get interrupted, may cause damage to the personnel and equipment and may also cause production losses.
There is, therefore, felt a need to prevent control system failures, improve efficiency of planned redundancy changeover and overcome the abovementioned drawbacks.
OBJECTS
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative.
Another object of the present disclosure is to provide a device and a method for maintenance of control systems.
Yet another object of the present disclosure is to provide a device and a method for prevention of control system failure.
Still another object of the present disclosure is to provide a device and a method for prevention of damage to the control system due to excessive noise, vibration, temperature, humidity, aging, and the like.
Still another object of the present disclosure is to provide a method that illustrates a mechanism for planned redundancy changeover before failure of components within the controller.
Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
The present disclosure envisages a device for monitoring a control system of a process plant. The device comprises: (i) a memory, (ii) a processing unit, (iii) a repository, (iv) a plurality of sensors, (v) an aging calculator and (vi) a conversion unit. The memory is configured to store a set of predetermined rules. The processing unit is configured to cooperate with the memory to receive the set of predetermined rules and is further configured to generate a set of processing commands. The repository is configured to store a predefined predictive model of the health of the control system based on environmental factors affecting the control system. The predictive model has a look up table containing combinations of the environmental factors and the corresponding health status of the control system. The plurality of sensors is configured to sense real-time environmental factors affecting the control system and further configured to generate a plurality of real-time sensed signals, wherein each signal corresponds to one of the factors. The conversion unit is configured to receive the plurality of sensed signals and further configured to generate a first set of digital values, wherein each element of the first set corresponds to a real-time factor. In an operative configuration, the processing unit is configured to cooperate with the memory, the repository, and the conversion unit to receive the set of predetermined rules, the predictive model, and the first set of digital values respectively. The processing unit (102) also cooperates with the aging calculator to calculate the aging of the control system based on the first set of digital values. The processing unit (102) is configured with a crawler and extractor which is adapted to crawl through the look up table using the first set of digital values to extract a health parameter of the control system corresponding to the first set of digital values. The processing unit is further configured to generate a first output signal related to the health parameter of the control system.
In an embodiment, the device further comprises: (i) a plurality of simulation units which is configured to simulate a plurality of real-time environmental factors that affect the control system and generate a set of simulated signals, wherein each simulated signal corresponds to the simulated environmental factors, (ii) a plurality of simulated sensors which is configured to sense the set of simulated signals and further configured to generate a set of simulated sensed signals. In this embodiment, the conversion unit is configured to receive the set of simulated sensed signals and is further configured to generate a second set of digital values, wherein each element of the second set corresponds to a simulated environmental factor. The processing unit is configured to receive the second set of digital values and is further configured to generate a second output signal related to the health parameter of the control system using the predictive model.
In another embodiment, the device further comprises a selector which is configured to, under the set of processing commands, select one mode of operation from a plurality of operating modes. The plurality of operating modes may include: (i) a real-time mode, wherein the selector receives the first set of digital values and transmits the first set of digital values to the processing unit, and (ii) a test mode, wherein the selector receives the second set of digital values and transmits the second set of digital values to the processing unit.
In yet another embodiment, the device further comprises a heat sink which is configured to absorb the heat produced by the device thereby reducing the temperature of the internal components.
In still another embodiment, the plurality of sensors in the device may be selected from the group consisting of a vibration sensor, a noise sensor, a dust and gas sensor, a temperature and humidity sensor, and an internet protocol (IP) camera.
In still another embodiment, the plurality of simulating units includes a vibration simulator, a noise generator / injector, a dust and gas simulator, and a temperature and humidity simulator.
In a preferred embodiment of the present disclosure, the device further comprises an in-built remote alerting unit which is configured to provide alerts to a user regarding critical events recognized by the alerting unit using the first output signal of the control system. The device may further comprise a remote monitoring and control facility which is configured to enable a user to make real-time decisions remotely by analyzing the data provided by a prediction and prevention model stored in the repository.
The present disclosure also envisages a method for implementation of the device for monitoring a control system.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
A device and a method for monitoring the health of a control system, of the present disclosure will now be described with the help of the accompanying drawing, in which:
Figure 1 illustrates a schematic block diagram of a device for monitoring, evaluation and maintenance of the control system, in accordance with an embodiment of the present disclosure;
Figure 2 illustrates an exploded view of the internal hardware of the device of Figure 1;
Figure 3 illustrates an isometric view of a processing unit and a carrier unit of the device of Figure 2;
Figure 4 illustrates an isometric view of a communication unit of the device of Figure 2;
Figure 5 illustrates a flow chart depicting a method for using the device disclosed in Figure 1 for monitoring the control system; and
Figure 6 illustrates a flow diagram depicting an operative configuration of the device for monitoring the control system, in accordance with an embodiment of the present disclosure.
LIST OF REFERENCE NUMERALS
Reference numeral References associated with reference numeral
100 Device
101 Sensor Board
101A Vibration sensor
101B Noise sensor
101C Dust and gas sensor
101D Temperature and humidity sensor
101E Aging calculator
101F Internet protocol (IP) camera
101G A Plurality of Sensor
102 Processing Unit
102A Main Processor
102B Auxiliary Processor
102C Repository
104 Carrier unit
104A & 104B At least two Ethernet Ports
104C A USB Port
106 Communication unit
106A A Plurality of Status Indicators
106B An RS232 Communication Port
106C An RS485 Communication Port
106D A plurality of Interfacing Connectors
108A Simulated Vibration Sensor
108B Simulated Noise Sensor
108C Simulated Dust and Gas Sensor
108D Simulated Temperature and Humidity Sensor
108E Simulated Aging Calculator
108F Simulated Internet Protocol (IP) Camera
109 Conversion Unit
110A Top Enclosure
110B Bottom Enclosure
112 A plurality of Simulation Units
112A Vibration Simulator
112B Noise Generator / Injector
112C Dust and Gas Simulator
112D Temperature and Humidity Simulator
114 Heat sink
DETAILED DESCRIPTION
A control system plays an important role in the operation of any process plant. Any failure in the control systems may lead to the shut-down of the process plant or damage to the personnel and equipment installed therein, or production losses. Every control system has a limited life span which can be quantified in terms of hours of operation. The life span or healthiness of every control system is dependent on a number of operational and environmental factors. Environmental factors such as excess vibrations, noise from external sources exceeding the rated limit for equipment’s, dust and gasses deposits on the electronic and machine components, temperature and humidity, are also major contributors in control system failures.
Further, the life span of every control system is affected by the failure of its components, structural failures in the system, and manufacturing defects in the system. In the long run, such environmental factors and localized failures can lead to an overall failure of the control system, which further affects the operation, productivity, and efficiency of the entire plant. Moreover, the failure of the control system also degrades the instruments connected in the plant. For example, in a power plant, frequent tripping of turbine or boiler can reduce the life of the components of the turbine, and in-turn damage the turbine itself.
The present disclosure envisages a device and a method for providing real time monitoring, evaluation and maintenance of a control system, by predicting failures in the control systems. The device of the present invention simulates the effects of external environmental parameters on the control systems. The device then runs a prediction model that collects the data from various sources simulating the external environmental parameters factors, to predict the possibility of failures in the control systems and check the health parameter of the control system. The device and method of the present disclosure is described with reference to Figure 1 through Figure 6.
Figure 1 illustrates a schematic block diagram of a device (100) for monitoring of a control system installed typically in a process plant, in accordance with an embodiment of the present disclosure. Figure 2 illustrates an exploded view of the internal hardware of the device (100) of Figure 1.
The device (100) may comprise a memory, a processing unit (102), a plurality of sensors (101G), a repository (102C), a conversion unit (109), a carrier unit (104), a communication unit (106), a sensor board (101), an enclosure (not exclusively labelled in the figures), a plurality of simulated sensors (108A-108F), an aging calculator (101E), and a plurality of simulating units (112). The enclosure of the device (100) is constituted of a top enclosure (110A) and a bottom enclosure (110B). The processing unit (102) is typically mounted on the carrier unit (104), which along with the communication unit (106) and the sensor board (101) sandwiched between the top enclosure (110A) and the bottom enclosure (110B) constitutes the internal hardware of the device (100) as shown in Figure 2.
In an embodiment, the device (100) is configured with two modes of operation – an offline simulation mode which is the test mode, and an online mode which is the real-time mode.
The device (100) and its functioning in the real-time mode of operation is described now. The memory of the device (100) is configured to store a set of predetermined rules. The processing unit (102) is configured to cooperate with the memory to receive the set of predetermined rules and is further configured to generate a set of processing commands. The repository (102C) is configured to store a predefined predictive model of the health of the control system based on environmental factors affecting the control system The predictive model has a look up table containing combinations of the environmental factors and the corresponding health status of the control system. The plurality of sensors (101G) is configured to sense real-time environmental factors such as excessive noise, vibration, temperature, humidity, aging, and the like, which affect the control system, and further configured to generate a plurality of sensed signals, wherein each signal corresponds to one of the factors. In an embodiment, the communication unit (106) enables interface with the control system and is configured to receive the generated plurality of sensed signals and transmit them to the conversion unit (109).
The conversion unit (109) is configured to receive the plurality of sensed signals and further configured to generate a first set of digital values, wherein each element of the first set corresponds to a real-time factor. In an operative configuration, the processing unit (102) receives the first set of digital values and the predefined predictive model. The processing unit (102) is configured to cooperate with an aging calculator (101E) and is configured to calculate aging of the control system based on the first set of digital values. The processing unit (102) is configured with a crawler and extractor which are adapted to crawl through the look up table using the first set of digital values to extract a health parameter of the control system corresponding to the first set of digital values. The processing unit (102) is further configured to generate a first output signal related to the health parameter of the control system.
The device (100) can also function in the offline simulation mode that is the test mode during maintenance period or during annual shutdown period. In order to perform tests in the offline simulation mode, it is necessary to generate signals which can be used to simulate the real time environmental factors such as excessive noise, vibration, temperature, humidity, aging, and the like, which affects the control system. In an embodiment, the plurality of simulating units (112) along with their connections, form the external hardware of the device (shown only in block diagrams). The plurality of simulating units (112) is configured to simulate a plurality of real-time environmental factors that affect the control system and generate a set of simulated signals, wherein each of the simulated signals corresponds to the simulated environmental factors. In an embodiment, a plurality of simulated sensors (108A-F) cooperates with the plurality of simulating units (112) and is configured to sense the simulated plurality of real-time environmental factors and generate a set of simulated sensed signals.
In this embodiment, the conversion unit (109) is configured to receive the set of simulated sensed signals and is further configured to generate a second set of digital values, wherein each element of the second set corresponds to a simulated environmental factor. The processing unit (102) is configured to receive the second set of digital values and is further configured to generate a second output signal related to the health parameter of the control system using the predictive model.
In a preferred embodiment, the device (100) comprises a selector (not shown in the figures) which is configured to select one mode of operation from a plurality of operating modes. In a preferred embodiment the device (100) has 2 modes of operation. The plurality of operating modes may include: (i) a real-time mode, wherein the selector receives the first set of digital values and transmits the first set of digital values to the processing unit (102), and (ii) a test mode, wherein the selector receives the second set of digital values and transmits the second set of digital values to the processing unit (102). In an embodiment, the conversion unit (109) is a part of the sensor board (101).
In an embodiment of the present disclosure, the sensor board (101) is interfaced with the carrier unit (104) and is configured to transmit the first and the second set of digital values to the processing unit (102) via the carrier unit (104). The processing unit (102) can be constituted of either a single processor or a group of at least two processors. In a preferred embodiment, the processing unit (102) includes a main processor (102A), and an auxiliary processor (102B). The main processor (102A) cooperates with the memory which is configured to store a set of pre-determined rules. The main processor (102A) of the processing unit (102) is configured to receive the set of predetermined rules from the memory and further configured to generate a set of processing commands sequentially. For example, the main processor (102A) is loaded with an operating system and a runtime application specific to the operation of a process plant including control logic for processes like monitoring the plant floor data, executing inputs and outputs to plant machinery, running PID’s and safety interlocks. The main processor (102A) is configured to control the components of the device (100) based on commands received from a user of the control system. The selector of the present device (100) is configured to select mode of operation in accordance with the set of processing commands. The repository (102C) which is configured to store the predictive model for the control system is in communication with the main processor (102A) and the auxiliary processor (102B).
In an embodiment, the auxiliary processor (102B) is pre-loaded with a prediction and prevention model (P&P Model) and works under system processing commands received from the main processor (102A). In an embodiment, the prediction and prevention model (P&P Model) is stored in the repository (102C) and is loaded in the auxiliary processor (102B) under system processing commands. The auxiliary processor (102B) receives the second set of digital values from the conversion unit (109) and fetches the predictive model from the repository (102C), in a test mode configuration. In one embodiment, the carrier unit (104) acts as an interfacing unit between the conversion unit (109) and the auxiliary processor (102B). The auxiliary processor (102B) is configured to crawl through the look-up table according to the second set of digital values in the predictive model and based on the P&P model generate the output signal related to the health parameter of the control signal. The auxiliary processor (102B) is further configured to either send the output signal to the repository (102C) for storing along with a log of the control system or display the corresponding data extracted from the prediction signals, via the communication unit (106), on the instructions of the main processor (102A) to the user of the control system to take corrective action if necessary.
In yet another embodiment, the internal hardware of the system includes a heat sink (114) also sandwiched and placed on top of the main processor (102A). The heat sink (114) is configured to absorb the heat produced by the device (100) thereby reducing the temperature of the internal components which will enhance the life of the components. In still another embodiment, the plurality of sensors (101G) include at least one of, a vibration sensor (101A), a noise sensor (101B), a dust and gas sensor (101C), a temperature and humidity sensor (101D), and an internet protocol (IP) camera (101F). In yet another embodiment, the plurality of simulated sensors include at least one of, a simulated vibration sensor (108A), a simulated noise sensor (108B), a simulated dust and gas sensor (108C), a simulated temperature and humidity sensor (108D), a simulated aging calculator (108E), and a simulated internet protocol (IP) camera (108F).
In yet another embodiment, the plurality of simulating units (112) includes at least one of, a vibration simulator (112A), a noise generator / injector (112B), a dust and gas simulator (112C), and a temperature and humidity simulator (112D).
In still another embodiment, the carrier unit (104) includes at least two Ethernet ports (104A & 104B) and a USB port (104C) configured for interfacing the processing unit (102). Figure 3 illustrates an isometric view of the processing unit (102) and the Carrier unit (104).
In yet another embodiment, the communication unit (106) includes a plurality of status indicators (106A), an RS232 Communication Port (106B), an RS485 Communication Port (106C), and a plurality of Interfacing Connectors (106D). Figure 4 illustrates an isometric view of the Communication unit (106).
An exemplary embodiment of the device (100) has the following configuration:
? an ARM processor with an A9, 667 MHz core acting as the processor unit (102);
? the repository (102C) is a 1 GB Flash memory;
? the processing unit (102) further includes a 512 Mb RAM and a 2 Kb FRAM;
? two 1 Gbps Ethernet Ports (104A & 104B), wherein the 1st port (104A) is configured for programming the main processor (102A) and the 2nd port (104B) is configured for the internet of things (IOT);
? the RS485 Communication Port (106C) is configured up to 115200 Kbps; and
? an External micro-SD card storage.
In still another embodiment, the device (100) includes an in-built remote alerting unit feature configured to provide alerts regarding critical events recognized by the first output signal to the user of the control system, via email or SMS connectivity. The P&P model segregates the alerts based on the output signal into three categories viz. Critical, Warning, and Normal sub-categorized under two main headings Component Failure and Environmental Failure. In an embodiment, a remote monitoring and control facility is provided by the device (100) to the control system that enables the user to make real-time decisions remotely by analyzing the data provided by the P&P model. In yet another embodiment, the device (100), in cases of emergency such as critical events and in the absence of corrective action from the user, is configured to take control of the control system, and is further configured to put the process plant in safe mode.
Figure 5 illustrates a flow chart depicting a method (150) for using the device (100) for monitoring the health of a control system. The method (150) for monitoring, evaluation and maintenance of the health of the control system comprises the following steps.
At block (152), the method (150) includes the step of storing a set of predetermined rules. At block (152), the method (150) also includes the step of generating a set of processing commands in accordance with the set of predetermined rules.
At block (154), the method (150) includes the step of storing a predefined predictive model of the health of the control system based on environmental factors affecting the control system in a repository.
At block (156), the method (150) includes the step of sensing the environmental factors of the control system by a plurality of sensors.
At block (158), the method (150) includes the step of sensing the simulated signals and further generating corresponding set of sensed signals.
At block (160), the method (150) includes the step of generating a set of sensed signals corresponding to the sensed environmental factors by the plurality of sensors.
At block (162), the method (150) includes the step of generating a first set of digital values corresponding to the set of generated sensed signals by a conversion unit.
At block (164), the method (150) includes the step of fetching the predictive model from a repository. At block (164), the method (150) further includes the step of crawling through a look up table using the first set of digital values to extract a health parameter of the control system.
At block (166), the method (150) includes the step of generating a first output signal related to the health parameter of the control system.
The method (150) further includes the steps of: (i) simulating a plurality of real-time environmental factors that affect the control system by a plurality of simulating units, (ii) generating a set of simulated signals corresponding to the simulated real-time environmental factors, (iii) sensing the set of simulated signals by a plurality of simulated sensors, (iv) generating a set of simulated sensed signals corresponding to the sensed simulated signals by the plurality of simulated sensors, (v) generating a second set of digital values corresponding to the set of generated simulated sensed signals by the conversion unit, and (vi) generating a first output signal related to the health parameter of the control system.
The method (150) may also include the step of selecting a mode of operation from a plurality of operating modes, wherein the plurality of operating modes includes: (i) a real-time mode, wherein a selector receives the second set of digital values and transmits the second set of digital values to the processing unit and (ii) a test mode, wherein the selector receives the first set of digital values and transmits the first set of digital values to the processing unit.
Figure 6 illustrates a flow diagram depicting an operative configuration of the device (100) for monitoring the health of a control system, in accordance with an embodiment of the present disclosure. The flow diagram depicts the sequence of steps required during redundancy based approach. In redundancy based approach a standby processing unit is used along with the processing unit. The standby processing unit operates in standby mode. In real time implementation, the standby processing unit is used when the processing unit fails. The processing unit operates in either real-time (active) or simulation mode. The processing unit and the standby processing unit are physically separated so that the impact of the environmental factors on such standby processing unit can be avoided.
At block (202A, and 202B), the method (200) includes the steps of selecting a mode of operation from the options of an active mode and a standby mode.
At block (204), the method (200) includes the steps of booting the device in active mode. In an embodiment, the processing unit (102) boots the device (100) in active mode.
At block (206), the method (200) includes the steps of initiating the offline simulation mode (test mode) by loading the P&P model and fetching the prediction model. In an embodiment, the processing unit (102) initiates the offline simulation mode by loading the P&P model and fetching the prediction model.
At block (208), the method (200) includes the steps of simulating a plurality of real-time environmental factors such as excessive noise, vibration, temperature, humidity, and aging, which affect the control system.
At block (210), the method (200) includes the steps of generating a set of simulated sensed signals by sensing the simulated plurality of real-time environmental factors, and converting the set of sensed simulated signals into their digital values and generating the second set of digital values.
At block (212), the method (200) includes the steps of crawling through a look up table according to the second set of digital values in the predictive model, and generating a second output signal based on the P&P model. In an embodiment, the processing unit (102) is configured to feed the second set of digital value in the predictive model and based on the P&P model generate a second output signal related to the health of the control system.
At block (214), the method (200) includes the steps of storing the second output signal along with a log of the control system in the repository (102C) or displaying the data extracted from the second output signals to the user of the control system to take corrective action if necessary.
At block (216), the method (200) includes the steps of re-selecting the mode of operation from the options of the offline simulation mode (test mode) and the online / real-time mode.
At block (218), the method (200) includes the steps of booting the device (100) in the online / real time mode when selected. In an embodiment, the processing unit (102) acts as the primary CPU and boots the device (100) in the online / real-time mode.
At block (220), the method (200) includes the steps of loading the runtime application specific to the operation of the process plant in the processing unit (102), which further includes the steps of:
at block (220A), sending boot command to the standby processing unit to boot as a redundant CPU in standby mode,
at blocks (220B, 220C), sending boot acknowledgement by the standby processing unit and receiving boot acknowledgement by the processing unit (102),
at blocks (220D 220E, 220F), sending synchronization command to the standby processing unit, acknowledgement of synchronization, and sending acknowledgement response to the processing unit (102) respectively, and
at block (220G), sending the stored first or second output signal and previous device log to the standby processing unit.
At block (222A, 222B), the method (200) includes the step of booting the processing unit (102) in active mode and the standby processing unit in stand-by mode.
At block (222C), the method (200) includes the step of handshaking between the processing unit (102) and the standby processing unit for health checking.
At block (224A, 224B), the method (200) includes the step of continuing to Active mode and Standby mode respectively.
At block (226A), the method (200) includes the step of problem with Active mode.
At block (226B), the method (200) includes the step of problem with Standby mode.
At block (228), the method (200) includes the step of breaking handshaking in case of either problem with Active or Standby Mode.
The method (200) may include the step in which the processing unit (102) goes into time-out and the standby processing unit switches to standalone mode after time-out and taking control of the communications bus. The method (200) provides a mechanism for planned redundancy changeover from the processing unit (102) to the standby processing unit before failure of parts of said processing unit (102).
The methods (200) may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions that perform particular functions or implement particular abstract data types. The method (200) may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices. Furthermore, the method (200) can be implemented in any suitable hardware, software, firmware, or combination thereof.
Thus, the device (100) of the present disclosure provides a low cost, computationally efficient, automated maintenance device which is self-learning and extrapolates the co-relation of various parameters to its predictive life or probability of failure dynamically in real time. The device (100) further detects possible fire or explosion conditions with the help of imaging camera to put plant into safe-mode or shutdown or take predefined actions to prevent spread of hazard or isolate it. The device (100) combines predictive aging with Mean Time Between Failures (MTBF) and Mean Time to Repair (MTTR) calculations using various types of sensors, and further runs a predictive routine to predict failure, aging, expected life and hence allows for a planned switch over or replacement of the control system parts or the entire control system itself. Further, the device (100) also allows to predict the aging of the control system based on the effect of multiple environmental factors such as temperature, humidity, vibration, dust, noise and gas affecting such control system concurrently.
The device (100) also performs actions to mitigate or overcome the adverse effects of the environmental factors on the control system. In an embodiment, if the part per million (ppm) level of any hazardous gas crosses the predetermined threshold value, the device (100) will start purging such hazardous gas out of the system to mitigate any adverse effect on the control system. In another embodiment, if the temperature near the device (100) increases beyond the predetermined threshold value, the device (100) will start the cooler mounted near the device (100), to reduce the temperature below the predetermined threshold value.
TECHNICAL ADVANCEMENTS
The present disclosure described herein above has several technical advantages including, but not limited to, the realization of a device and a method that:
? provides online monitoring, evaluation and predictive maintenance of control systems;
? prevents failure of control systems;
? senses all the parameters of control systems on a continuous basis;
? simulates parameters like temperature, vibration, noise to check performance and response of controllers of control system during plant shutdown and maintenance;
? is self-learning and extrapolates the co-relation of various parameters to its predictive life or probability of failure dynamically in real time;
? provides an efficient mechanism for planned redundancy changeover before failure of parts of the controller;
? allows pre-planning of spares or redundant controller procurement,
? uses a non-redundant controller if pre-planned shutdown or switch over is allowed, while significantly reducing the system cost,
? detects hazardous accident in the process plant and automatically deals with the hazard; and
? predicts aging of control systems and plan for replacement / obsolescence of parts of control systems or the control systems themselves.
The foregoing disclosure has been described with reference to the accompanying embodiments which do not limit the scope and ambit of the disclosure. The description provided is purely by way of example and illustration.
The embodiments hereinabove and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing models are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The foregoing description of the specific embodiments so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.
The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results.
Any discussion of documents, acts, materials, devices, articles or the like that has been included in this specification is solely for the purpose of providing a context for the disclosure. It is not to be taken as an admission that any or all of these matters form a part of the prior art base or were common general knowledge in the field relevant to the disclosure as it existed anywhere before the priority date of this application.
The numerical values mentioned for the various physical parameters, dimensions or quantities are only approximations and it is envisaged that the values higher/lower than the numerical values assigned to the parameters, dimensions or quantities fall within the scope of the disclosure, unless there is a statement in the specification specific to the contrary.
While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

,CLAIMS:WE CLAIM:
1. A device (100) for monitoring the health of a control system, said device (100) comprising:
• a memory configured to store a set of predetermined rules;
• a repository (102C) configured to store a predefined predictive model of the health of said control system based on environmental factors affecting said control system, said predictive model having a look up table containing combinations of said environmental factors and the corresponding health status of said control system;
• a plurality of sensors (101G) configured to sense real-time environmental factors affecting said control system and further configured to generate a plurality of real-time sensed signals, each signal corresponding to one of said environmental factors;
• a conversion unit (109) configured to receive said plurality of sensed signals and further configured to generate a first set of digital values, each element of said first set corresponds to a real-time environmental factor;
• an aging calculator (101E) configured to calculate aging of said control system based on said first set of digital values;
• a processing unit (102) configured to cooperate with said memory, said repository (102C), said conversion unit (109) and said aging calculator (101E) to receive the set of predetermined rules, said predictive model, and said first set of digital values respectively;
a crawler and extractor configured in said processing unit (102) adapted to crawl through said look up table using said first set of digital values to extract a health parameter of said control system corresponding to said first set of digital values, said processing unit (102) further configured to generate a first output signal related to the health parameter of said control system.
2. The device (100) as claimed in claim 1, wherein said device (100) further comprises:
• a plurality of simulation units (112A-112D) configured to simulate a plurality of real-time environmental factors that affect said control system and generate a set of simulated signals, each simulated signal corresponding to said simulated environmental factors; and
• a plurality of simulated sensors (108A-108F) configured to sense said set of simulated signals and further configured to generate a set of simulated sensed signals;
wherein said conversion unit (109) is configured to receive said set of simulated sensed signals and further configured to generate a second set of digital values, each element of said second set corresponds to a simulated environmental factor; and
said processing unit (102) is configured to receive said second set of digital values and is further configured to generate a second output signal related to the health parameter of said control system using said predictive model.
3. The device (100) as claimed in claim 1 or claim 2, further comprises a selector which is configured to, under the set of processing commands, select one mode of operation from a plurality of operating modes, wherein said plurality of operating modes includes:
o a real-time mode, wherein said selector receives said first set of digital values and transmits said first set of digital values to said processing unit (102); and
o a test mode, wherein said selector receives said second set of digital values and transmits said second set of digital values to said processing unit (102).
4. The device (100) as claimed in claim 1, wherein said plurality of sensors (101G) includes a vibration sensor (101A), a noise sensor (101B), a dust and gas sensor (101C), a temperature and humidity sensor (101D), and an internet protocol (IP) camera (101F), and said plurality of simulating units (112) includes a vibration simulator (112A), a noise generator (112B), a dust and gas simulator (112C), and a temperature and humidity simulator (112D).
5. The device (100) as claimed in claim 1, further comprises a heat sink (114) configured to absorb the heat produced by the device (100) thereby reducing the temperature of the internal components.
6. The device (100) as claimed in claim 1, further comprises an in-built remote alerting unit configured to provide alerts to a user regarding critical events recognized by said alerting unit using said first output signal of said control system.
7. The device (100) as claimed in claim 1, further comprises a remote monitoring and control facility configured to enable a user to make real-time decisions remotely by analyzing the data provided by a prediction and prevention model.
8. The device (100) as claimed in claim 1, wherein said device (100) is configured to predict the aging of said control system based on the effect of multiple environmental factors such as temperature, humidity, vibration, dust, noise and gas affecting such control system concurrently, and further configured to perform actions to mitigate or overcome the adverse effects of the environmental factors on said control system.
9. A method (150) for monitoring the health of a control system, said method comprising the steps of:
• storing a set of predetermined rules in a memory;
• generating a set of processing commands in accordance with the set of predetermined rules by a processing unit;
• storing a predefined predictive model of the health of said control system based on environmental factors affecting said control system in a repository;
• sensing real-time environmental factors of said control system by a plurality of sensors;
• generating a set of sensed signals corresponding to said sensed real-time environmental factors by said plurality of sensors;
• generating a first set of digital values corresponding to said set of generated sensed signals by a conversion unit;
• fetching said predictive model from a repository;
• crawling through a look up table using said first set of digital values to extract a health parameter of said control system; and
• generating a first output signal related to the health parameter of said control system.
10. The method (150) as claimed in claim 9, further comprises the steps of:
• simulating a plurality of real-time environmental factors that affect said control system by a plurality of simulating units;
• generating a set of simulated signals corresponding to said simulated real-time environmental factors;
• sensing said set of simulated signals by a plurality of simulated sensors;
• generating a set of simulated sensed signals corresponding to said sensed simulated signals by said plurality of simulated sensors;
• generating a second set of digital values corresponding to said set of generated simulated sensed signals by said conversion unit; and
• generating a second output signal related to the health parameter of said control system.
11. The method as claimed in claim 9, further illustrating a mechanism for planned redundancy changeover from said processing unit (102) to a standby processing unit before failure of parts of said processing unit (102).