SYSTEM AND METHOD FOR DETECTING AND/OR DIAGNOSING FAULTS IN MULTI-VARIABLE SYSTEMS

• US 20120185728A1
• Filed: 12/23/2011
• Published: 07/19/2012
• Est. Priority Date: 12/24/2010
• Status: Active Grant

• Current Assignee: Commonwealth Scientific & Industrial Research Organisation (Commonwealth Of Australia As Represented By Department Of Industry, Innovation, Climate Change, Science, Research And Tertiary Education)
• Original Assignee: Commonwealth Scientific & Industrial Research Organisation (Commonwealth Of Australia As Represented By Department Of Industry, Innovation, Climate Change, Science, Research And Tertiary Education)
• Inventors: Guo, Ying, Li, Jiaming, West, Sam, Wall, Joshua, Platt, Glenn
• Granted Patent: US 9,366,451 B2
• Time in Patent Office: Days
• US Class Current: 714/26
• CPC Class Codes: F24F 11/30 for purposes related to the operation of the system, e.g. for safety or monitoring
• CPC Class Codes: F24F 11/32 Responding to malfunctions or emergencies
• CPC Class Codes: F24F 11/38 Failure diagnosis
• CPC Class Codes: F24F 11/63 Electronic processing
• CPC Class Codes: F24F 2140/00 Control inputs relating to system states
• CPC Class Codes: G05B 2219/2614 HVAC, heating, ventillation, climate control
• 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: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A method for detecting faulty operation of a multi-variable system is described. The method includes receiving operational data from a plurality of components of the multi-variable system and processing the operational data in accordance with a plurality of dynamic machine learning fault detection models to generate a plurality of fault detection results. Each fault detection model uses a plurality of variables to model one or more components of the multi-variable system and is adapted to detect normal or faulty operation of an associated component or set of components of the multi-variable system. The plurality of fault detection results are output.

Claims

1. A method for detecting faulty operation of a HVAC system, the method including:

receiving operational data from a plurality of components of the HVAC system;

processing the operational data in accordance with a plurality of dynamic machine learning fault detection models to generate a plurality of fault detection results,

each fault detection model using a plurality of variables to model one or more components of the HVAC system and being adapted to detect normal or faulty operation of an associated component or set of components of the HVAC system; and

outputting the plurality of fault detection results.

2. A method according to claim 1, wherein at least one fault detection model is adapted to detect normal operation of its associated component or set of components.

3. A method according to claim 1, wherein at least one fault detection model is adapted to detect faulty operation of its associated component or set of components.

4. A method according to claim 1, wherein at least two fault detection models are adapted to detect normal or faulty operation of different associated components or sets of components.

5. A method according to claim 1, wherein the plurality of fault detection models includes:

at least one general fault detection model adapted to detect normal or faulty operation of a general set of components of the HVAC system; and

at least one specific fault detection model adapted to detect normal or faulty operation of a specific component or set of components of the HVAC system, wherein

each specific fault detection model is associated with a general fault detection model, and

the component or set of components associated with the specific fault detection model is a subset of or related to the set of components of the associated general fault detection model.

6. A method according to claim 5, wherein one of the at least one general fault detection models is adapted to detect normal or faulty operation of the HVAC system as a whole.

7. A method according to claim 5, wherein the operational data is only processed in accordance with the at least one specific fault detection model if processing the operational data in accordance with the associated general fault detection model indicates the existence of a fault.

8. A method according to claim 5, wherein if both a general fault detection process and a specific fault detection process associated with that general fault detection process indicate faulty operation of the HVAC system, the fault is diagnosed as a fault in the component or set of components associated with the specific fault detection process.

9. A method according to claim 1, wherein at least one of the plurality of fault detection models is selected from a group including dynamic Bayesian network based fault detection models and hidden Markov model based fault detection models.

10. A method according to claim 1, further including:

processing the plurality of fault detection results in accordance with a data fusion process to generate a fused fault detection result, the fused fault detection result including information as to whether a fault is perceived in the operation of the HVAC system; and

outputting said fused fault detection result.

11. A method according to claim 10, wherein the data fusion process is a Dempster-Shafer based data fusion process.

12. A method according to claim 1, further including:

for each of the plurality of fault detection results calculating an associated confidence level; and

using the calculated confidence levels to select which of the plurality of fault detection results is or are likely to be correct.

13. A method according to claim 12, further including fusing said plurality of confidence levels to provide a fused confidence level result;

and

using said fused confidence level result along with said fused fault detection result to determine the existence of and diagnose a fault in the HVAC system.

14. A method according to claim 12, wherein said confidence levels are belief masses calculated according to a Dempster-Shafer based process.

15. A method according to claim 12, further including:

selecting one or more of the fault detection results based on the confidence level associated with the fault detection result;

generating a set of variables, the set of variables including the variables modelled by the fault detection models which generated the selected fault detection results; and

outputting said set of variables for use in diagnose a fault in the HVAC system.

16. A method according to claim 1, further including:

processing the operational data in accordance with at least one of the fault detection models multiple times to generate multiple results from said at least one fault detection model, and

clustering the multiple results in accordance with a clustering algorithm in order to increase the reliability of the fault detection result.

17. A method according to claim 16, wherein the clustering algorithm is a K-means clustering algorithm.

18. A method according to claim 1, wherein if two fault detection results are contradictory, the method further includes:

flagging one or both of the fault detection models yielding the contradictory results; and

modifying and/or retraining the or each flagged fault detection model.

19. A method according to claim 1, wherein if a fault detection result is identified as being a false alarm, the method further includes:

flagging the fault detection model yielding the result identified to be a false alarm; and

modifying and/or retraining the flagged fault detection models.

20. A method according to claim 16, wherein if two fault detection results are contradictory, the method further includes:

flagging one or both of the fault detection models yielding the contradictory results;

modifying and/or retraining the or each flagged fault detection model; and

using said flagged fault detection models to modify the clustering algorithm in order to improve the clustering of the multiple search results.

21. Computer readable instructions executable by a computer processor to implement the method according to claim 1.

22. A computer readable storage medium readable by a computer processor, the storage medium storing instructions according to claim 21.

23. A computer system including a microprocessor and associated memory, a communications interface, at least one input interface, and at least one output interface, the memory storing instructions that, when executed, cause the computer system to implement the method of claim 1.

48. A method according to claim 1, wherein the HVAC system is configured to control environmental conditions in connection with human thermal comfort in a building.

24. A method for detecting faulty operation of a multi-variable system, the method including:

receiving operational data from a plurality of components of the multi-variable system;

processing the operational data in accordance with a plurality of dynamic machine learning fault detection models to generate a plurality of fault detection results,

each fault detection model using a plurality of variables to model one or more components of the multi-variable system and being adapted to detect normal or faulty operation of an associated component or set of components of the multi-variable system; and

outputting the plurality of fault detection results.

25. A method according to claim 24, wherein at least one fault detection model is adapted to detect normal operation of its associated component or set of components.

26. A method according to claim 24, wherein at least one fault detection model is adapted to detect faulty operation of its associated component or set of components.

27. A method according to claim 24, wherein at least two fault detection models are adapted to detect normal or faulty operation of different associated components or sets of components.

28. A method according to claim 24, wherein the plurality of fault detection models includes:

at least one general fault detection model adapted to detect normal or faulty operation of a general set of components of the multi-variable system; and

at least one specific fault detection model adapted to detect normal or faulty operation of a specific component or set of components of the multi-variable system, wherein

each specific fault detection model is associated with a general fault detection model, and

the component or set of components associated with the specific fault detection model is a subset of or related to the set of components of the associated general fault detection model.

29. A method according to claim 28, wherein one of the at least one general fault detection models is adapted to detect normal or faulty operation of the multi-variable system as a whole.

30. A method according to claim 28, wherein the operational data is only processed in accordance with the at least one specific fault detection model if processing the operational data in accordance with the associated general fault detection model indicates the existence of a fault.

31. A method according to claim 28, wherein if both a general fault detection process and a specific fault detection process associated with that general fault detection process indicate faulty operation of the multi-variable system, the fault is diagnosed as a fault in the component or set of components associated with the specific fault detection process.

32. A method according to claim 24, wherein at least one of the plurality of fault detection models is selected from a group including dynamic Bayesian network based fault detection models and hidden Markov model based fault detection models.

33. A method according to claim 24, further including:

processing the plurality of fault detection results in accordance with a data fusion process to generate a fused fault detection result, the fused fault detection result including information as to whether a fault is perceived in the operation of the multi-variable system; and

outputting said fused fault detection result.

34. A method according to claim 33, wherein the data fusion process is a Dempster-Shafer based data fusion process.

35. A method according to claim 24, further including:

for each of the plurality of fault detection results calculating an associated confidence level; and

using the calculated confidence levels to select which of the plurality of fault detection results is or are likely to be correct.

36. A method according to claim 35, further including fusing said plurality of confidence levels to provide a fused confidence level result;

and

using said fused confidence level result along with said fused fault detection result to determine the existence of and diagnose a fault in the multi-variable system.

37. A method according to claim 35, wherein said confidence levels are belief masses calculated according to a Dempster-Shafer based process.

38. A method according to claim 35, further including:

selecting one or more of the fault detection results based on the confidence level associated with the fault detection result;

generating a set of variables, the set of variables including the variables modelled by the fault detection models which generated the selected fault detection results; and

outputting said set of variables for use in diagnose a fault in the multi-variable system.

39. A method according to claim 24, further including:

processing the operational data in accordance with at least one of the fault detection models multiple times to generate multiple results from said at least one fault detection model, and

clustering the multiple results in accordance with a clustering algorithm in order to increase the reliability of the fault detection result.

40. A method according to claim 39, wherein the clustering algorithm is a K-means clustering algorithm.

41. A method according to claim 24, wherein if two fault detection results are contradictory, the method further includes:

flagging one or both of the fault detection models yielding the contradictory results; and

modifying and/or retraining the or each flagged fault detection model.

42. A method according to claim 24, wherein if a fault detection result is identified as being a false alarm, the method further includes:

flagging the fault detection model yielding the result identified to be a false alarm; and

modifying and/or retraining the flagged fault detection models.

43. A method according to claim 41, wherein if two fault detection results are contradictory, the method further includes:

flagging one or both of the fault detection models yielding the contradictory results;

modifying and/or retraining the or each flagged fault detection model; and

using said flagged fault detection models to modify the clustering algorithm in order to improve the clustering of the multiple search results.

44. Computer readable instructions executable by a computer processor to implement the method according to claim 24.

45. A computer readable storage medium readable by a computer processor, the storage medium storing instructions according to claim 44.

46. A computer system including a microprocessor and associated memory, a communications interface, at least one input interface, and at least one output interface, the memory storing instructions that, when executed, cause the computer system to implement the method of claim 24.

47. A method according to claim 24, wherein the multi-variable system is selected from a group including:

a HVAC system;

a refrigeration system;

an energy distribution system;

an automated lighting system;

a security/alarm system;

a smoke/fire detection system; and

a gas detection system.