DISTINGUISHING BETWEEN SENSOR AND PROCESS FAULTS IN A SENSOR NETWORK WITH MINIMAL FALSE ALARMS USING A BAYESIAN NETWORK BASED METHODOLOGY

• US 20120215450A1
• Filed: 02/22/2012
• Published: 08/23/2012
• Est. Priority Date: 02/23/2011
• Status: Active Application

• Current Assignee: Board of Regents of the University of Texas System (University of Texas System)
• Original Assignee: Board of Regents of the University of Texas System (University of Texas System)
• Inventors: Tesar, Delbert, Ashok, Pradeepkumar, Krishnamoorthy, Ganesh
• Application Number:
  US13/402,084
• Publication Number:
  US 20120215450A1
• Time in Patent Office: Days
• US Class Current: 702/9
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: G05B 23/0262 Confirmation of fault detection, e.g. extra checks to confirm that a failure has indeed occurred

Abstract

A method, system and computer program product for distinguishing between a sensor fault and a process fault in a physical system and use the results obtained to update the model. A Bayesian network is designed to probabilistically relate sensor data in the physical system which includes multiple sensors. The sensor data from the sensors in the physical system is collected. A conditional probability table is derived based on the collected sensor data and the design of the Bayesian network. Upon identifying anomalous behavior in the physical system, it is determined whether a sensor fault or a process fault caused the anomalous behavior using belief values for the sensors and processes in the physical system, where the belief values indicate a level of trust regarding the status of its associated sensors and processes not being faulty.

Claims

1. A method for distinguishing between a sensor fault and a process fault in a physical system, the method comprising:

designing a Bayesian network to probabilistically relate sensor data in said physical system, wherein said physical system comprises a plurality of sensors;

collecting said sensor data from said plurality of sensors in said physical system;

deriving a conditional probability table based on said collected sensor data and said design of said Bayesian network;

identifying anomalous behavior in said physical system; and

determining, by a processor, one of said sensor fault and said process fault caused said identified anomalous behavior using belief values for said plurality of sensors and a plurality of processes in said physical system, wherein said belief values indicate a level of trust regarding the status of its associated sensors and processes not being faulty.

2. The method as recited in claim 1 further comprising:

inferring a value to be generated by one of said plurality of sensors of said physical system using one or more values sampled from one or more other sensors of said plurality of sensors and using one or more processes of said plurality of processes.

3. The method as recited in claim 2 further comprising:

increasing said belief values for said one or more other sensors and said one or more processes used in inferring said value to be generated by said one of said plurality of sensors in response to said value to be generated by said one of said plurality of sensors matching a value sampled for said one of said plurality of sensors.

4. The method as recited in claim 2 further comprising:

decreasing said belief values for said one or more other sensors and said one or more processes used in inferring said value to be generated by said one of said plurality of sensors in response to said value to be generated by said one of said plurality of sensors not matching a value sampled for said one of said plurality of sensors.

5. The method as recited in claim 1 further comprising:

iteratively inferring a value to be generated by a different sensor of said plurality of sensors using one or more values sampled from one or more other sensors of said plurality of sensors and using one or more processes of said plurality of processes.

6. The method as recited in claim 5 further comprising:

increasing at an end of an iteration said belief values for said one or more other sensors and said one or more processes used in inferring said value to be generated by one of said plurality of sensors in response to said value to be generated by said one of said plurality of sensors matching a value sampled for said one of said plurality of sensors.

7. The method as recited in claim 5 further comprising:

decreasing at an end of an iteration said belief values for said one or more other sensors and said one or more processes used in inferring said value to be generated by one of said plurality of sensors in response to said value to be generated by said one of said plurality of sensors not matching a value sampled for said one of said plurality of sensors.

8. The method as recited in claim 1 further comprising:

identifying a sensor of said plurality of sensors that is important for operational reasons; and

maximizing a number of links directly inbound/outbound onto a node for said identified sensor in said Bayesian network.

9. The method as recited in claim 1 further comprising:

identifying a first sensor of said plurality of sensors that is most likely to provide a best estimate of a second sensor of said plurality of sensors based on node distance between a node of said first sensor and a node of said second sensor in said Bayesian network.

10. The method as recited in claim 1 further comprising:

identifying a first sensor of said plurality of sensors that is most likely to provide a best estimate of a second sensor of said plurality of sensors based on connection and link strength between a node of said first sensor and a node of said second sensor in said Bayesian network.

11. The method as recited in claim 1, wherein said physical system comprises one of the following:

a nuclear reactor, an airplane, a wind turbine, a power distribution system, an automobile, a drilling rig, a chemical plant and a patient health monitoring system.

12. The method as recited in claim 1 further comprising:

updating said conditional probability table in response to determining said process fault caused said identified anomalous behavior.

13. The method as recited in claim 1 further comprising:

introducing additional nodes, representing redundant sensors, into said Bayesian network.

14. The method as recited in claim 1 further comprising:

displaying an indication that one of said sensor fault and said process fault caused said identified anomalous behavior.

15. A computer program product embodied in a computer readable storage medium for distinguishing between a sensor fault and a process fault in a physical system, the computer program product comprising the programming instructions for:

designing a Bayesian network to probabilistically relate sensor data in said physical system, wherein said physical system comprises a plurality of sensors;

collecting said sensor data from said plurality of sensors in said physical system;

deriving a conditional probability table based on said collected sensor data and said design of said Bayesian network;

identifying anomalous behavior in said physical system; and

determining one of said sensor fault and said process fault caused said identified anomalous behavior using belief values for said plurality of sensors and a plurality of processes in said physical system, wherein said belief values indicate a level of trust regarding the status of its associated sensors and processes not being faulty.

16. The computer program product as recited in claim 15 further comprising the programming instructions for:

inferring a value to be generated by one of said plurality of sensors of said physical system using one or more values sampled from one or more other sensors of said plurality of sensors and using one or more processes of said plurality of processes.

17. The computer program product as recited in claim 16 further comprising the programming instructions for:

increasing said belief values for said one or more other sensors and said one or more processes used in inferring said value to be generated by said one of said plurality of sensors in response to said value to be generated by said one of said plurality of sensors matching a value sampled for said one of said plurality of sensors.

18. The computer program product as recited in claim 16 further comprising the programming instructions for:

decreasing said belief values for said one or more other sensors and said one or more processes used in inferring said value to be generated by said one of said plurality of sensors in response to said value to be generated by said one of said plurality of sensors not matching a value sampled for said one of said plurality of sensors.

19. The computer program product as recited in claim 15 further comprising the programming instructions for:

iteratively inferring a value to be generated by a different sensor of said plurality of sensors using one or more values sampled from one or more other sensors of said plurality of sensors and using one or more processes of said plurality of processes.

20. The computer program product as recited in claim 19 further comprising the programming instructions for:

increasing at an end of an iteration said belief values for said one or more other sensors and said one or more processes used in inferring said value to be generated by one of said plurality of sensors in response to said value to be generated by said one of said plurality of sensors matching a value sampled for said one of said plurality of sensors.

21. The computer program product as recited in claim 19 further comprising the programming instructions for:

decreasing at an end of an iteration said belief values for said one or more other sensors and said one or more processes used in inferring said value to be generated by one of said plurality of sensors in response to said value to be generated by said one of said plurality of sensors not matching a value sampled for said one of said plurality of sensors.

22. The computer program product as recited in claim 15, wherein said physical system comprises one of the following:

a nuclear reactor, an airplane, a wind turbine, a power distribution system, an automobile, a drilling rig, a chemical plant and a patient health monitoring system.

23. The computer program product as recited in claim 15 further comprising the programming instructions for:

updating said conditional probability table in response to determining said process fault caused said identified anomalous behavior.

24. The computer program product as recited in claim 15 further comprising the programming instructions for:

displaying an indication that one of said sensor fault and said process fault caused said identified anomalous behavior.

25. A system, comprising:

a memory unit for storing a computer program for distinguishing between a sensor fault and a process fault in a physical system; and

a processor coupled to said memory unit, wherein said processor, responsive to said computer program, comprises;

circuitry for designing a Bayesian network to probabilistically relate sensor data in said physical system, wherein said physical system comprises a plurality of sensors;

circuitry for collecting said sensor data from said plurality of sensors in said physical system;

circuitry for deriving a conditional probability table based on said collected sensor data and said design of said Bayesian network;

circuitry for identifying anomalous behavior in said physical system; and

circuitry for determining one of said sensor fault and said process fault caused said identified anomalous behavior using belief values for said plurality of sensors and a plurality of processes in said physical system, wherein said belief values indicate a level of trust regarding the status of its associated sensors and processes not being faulty.

26. The system as recited in claim 25, wherein said processor further comprises:

circuitry for inferring a value to be generated by one of said plurality of sensors of said physical system using one or more values sampled from one or more other sensors of said plurality of sensors and using one or more processes of said plurality of processes.

27. The system as recited in claim 26, wherein said processor further comprises:

circuitry for increasing said belief values for said one or more other sensors and said one or more processes used in inferring said value to be generated by said one of said plurality of sensors in response to said value to be generated by said one of said plurality of sensors matching a value sampled for said one of said plurality of sensors.

28. The system as recited in claim 26, wherein said processor further comprises:

circuitry for decreasing said belief values for said one or more other sensors and said one or more processes used in inferring said value to be generated by said one of said plurality of sensors in response to said value to be generated by said one of said plurality of sensors not matching a value sampled for said one of said plurality of sensors.

29. The system as recited in claim 25, wherein said processor further comprises:

circuitry for iteratively inferring a value to be generated by a different sensor of said plurality of sensors using one or more values sampled from one or more other sensors of said plurality of sensors and using one or more processes of said plurality of processes.

30. The system as recited in claim 29, wherein said processor further comprises:

circuitry for increasing at an end of an iteration said belief values for said one or more other sensors and said one or more processes used in inferring said value to be generated by one of said plurality of sensors in response to said value to be generated by said one of said plurality of sensors matching a value sampled for said one of said plurality of sensors.

31. The system as recited in claim 29, wherein said processor further comprises:

circuitry for decreasing at an end of an iteration said belief values for said one or more other sensors and said one or more processes used in inferring said value to be generated by one of said plurality of sensors in response to said value to be generated by said one of said plurality of sensors not matching a value sampled for said one of said plurality of sensors.

32. The system as recited in claim 25, wherein said physical system comprises one of the following:

a nuclear reactor, an airplane, a wind turbine, a power distribution system, an automobile, a drilling rig, a chemical plant and a patient health monitoring system.

33. The system as recited in claim 25, wherein said processor further comprises:

circuitry for updating said conditional probability table in response to determining said process fault caused said identified anomalous behavior.

34. The system as recited in claim 25, wherein said processor further comprises:

circuitry for displaying an indication that one of said sensor fault and said process fault caused said identified anomalous behavior.