SYSTEMS AND METHODS FOR EVENT DETECTION AND DIAGNOSIS

• US 20150219530A1
• Filed: 12/01/2014
• Published: 08/06/2015
• Est. Priority Date: 12/23/2013
• Status: Abandoned Application

• Current Assignee: ExxonMobil Research & Engineering Company (Exxon Mobil Corporation)
• Original Assignee: ExxonMobil Research & Engineering Company (Exxon Mobil Corporation)
• Inventors: Li, Weichang, O'CONNOR, Thomas F., DASH, Sourabh K., SOMMERS, Jeffrey J.
• Application Number:
  US14/556,458
• Publication Number:
  US 20150219530A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01M 99/008 by doing functionality tests
• CPC Class Codes: G05B 23/0243 model based detection method, e.g. first-principles knowledge model
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04

Abstract

Detection of event conditions in an industrial plant includes receiving process data corresponding to one or more sensors, estimating normal statistics from the process data, estimating abnormal statistics from the process data with potentially abnormal operation of the one or more components, determining a fault model from the estimated normal and abnormal statistics, the fault model including a learning matrix, one or more fault indices indicating a likelihood of an occurrence of one or more fault events, and a fault threshold corresponding to the one or more sensors, determining one or more further fault indices from the further process data; applying the fault threshold to the one or more further fault indices, and indicating a further occurrence of the one or more fault events when a magnitude of the one or more further fault indices exceeds the fault threshold corresponding to the one or more sensors.

Claims

1. A method for detection of event conditions in an industrial plant, comprising:

receiving process data corresponding to one or more sensors;

estimating normal statistics from the process data associated with normal operation of one or more components corresponding to the one or more sensors;

estimating abnormal statistics from the process data with potentially abnormal operation of the one or more components;

determining, by a model processor, a fault model from the estimated normal and abnormal statistics, the fault model comprising a learning matrix, one or more fault indices indicating a likelihood of an occurrence of one or more fault events, and a fault threshold corresponding the one or more sensors;

receiving, by a detector processor operably coupled to the model processor, the one or more fault indices, the fault threshold and further process data from the one or more sensors;

determining one or more further fault indices from the further process data;

applying the fault threshold to the one or more further fault indices; and

indicating a further occurrence of the one or more fault events when a magnitude of the one or more further fault indices exceeds the fault threshold corresponding to the one or more sensors.

2. The method of claim 1, wherein estimating the abnormal statistics comprises performing a minimum mean squared error (MMSE) fault estimate on the process data.

3. The method of claim 1, wherein determining the one or more further fault indices comprises performing one or more of Neyman-Pearson Hypothesis testing and generalized likelihood ratio testing on the further process data.

4. The method of claim 1, further comprising dynamically adjusting the fault model using the further process data.

5. The method of claim 4, wherein dynamically adjusting the fault model comprises continuously updating the learning matrix based on updated estimates of the normal statistics and the abnormal statistics.

6. The method of claim 4, wherein dynamically adjusting the fault model comprises adjusting the fault threshold using the one or more further fault indices associated with normal and abnormal segments of the further process data received over a predetermined time window.

7. The method of claim 1, wherein the fault model further comprises a fault sensor map to relate the one or more sensors to the one or more components, the method further comprising, when the fault event is indicated, determining, by a diagnosis processor, a faulty component corresponding to the at least one of the one or more sensors.

8. The method of claim 7, wherein the fault model further comprises a fault dictionary stored in a database or a memory to relate patterns of the determined faulty components to the one or more fault events and a label having an operational meaning.

9. The method of claim 1, wherein the fault model further comprises a root cause map to relate first sensor conditions corresponding to a first fault event of a first component to second sensor conditions corresponding to a second fault event of a second component, the method further comprising, determining, by a root cause processor, a faulty system or group of systems corresponding to the related first and second sensor conditions.

10. The method of claim 1, further comprising partitioning the one or more sensors based at least in part on a statistical dependence among the one or more sensors from a corresponding type of measurement performed.

11. The method of claim 1, further comprising partitioning the one or more sensors by a statistical and dynamical characterization of the one or more fault events.

12. A method for identification of event conditions in an industrial plant, comprising:

receiving process data corresponding to one or more sensors;

estimating normal statistics from the process data associated with normal operation of one or more components corresponding to the one or more sensors;

estimating abnormal statistics from the process data with potentially abnormal operation of the one or more components;

determining, by a model processor, a fault model from the estimated normal and abnormal statistics, the fault model comprising a learning matrix, one or more fault indices indicating a likelihood of an occurrence of one or more fault events, and a fault threshold corresponding the one or more sensors;

receiving, by a detector processor operably coupled to the model processor, the one or more fault indices, the fault threshold and further process data from the one or more sensors;

determining one or more further fault indices from the further process data;

applying the fault threshold to the one or more further fault indices;

indicating a further occurrence of the one or more fault events when a magnitude of the one or more further fault indices exceeds the fault threshold corresponding to the one or more sensors;

relating the one or more components to the fault threshold corresponding to the one or more sensors; and

identifying a type of the one or more fault events based on the relation of the one or more components to the fault threshold corresponding to the one or more sensors.

13. The method of claim 12, wherein estimating the abnormal statistics comprises performing a minimum mean squared error (MMSE) fault estimate on the process data.

14. The method of claim 12, wherein determining the one or more further fault indices comprises performing one or more of Neyman-Pearson Hypothesis testing and generalized likelihood ratio testing on the further process data.

15. The method of claim 12, further comprising dynamically adjusting the fault model using the further process data.

16. The method of claim 15, wherein dynamically adjusting the fault model comprises continuously updating the learning matrix based on updated estimates of the normal statistics and the abnormal statistics.

17. The method of claim 15, wherein dynamically adjusting the fault model comprises adjusting the fault threshold using the one or more further fault indices associated with normal and abnormal segments of the further process data received over a predetermined time window.

18. The method of claim 12, wherein the fault model further comprises a fault sensor map to relate the one or more sensors to the one or more components, the method further comprising, when the fault event is indicated, determining, by a diagnosis processor, a faulty component corresponding to the at least one of the one or more sensors.

19. The method of claim 18, wherein the fault model further comprises a fault dictionary stored in a database or a memory to relate patterns of the determined faulty components to the one or more fault events and a label having an operational meaning.

20. The method of claim 12, wherein the fault model further comprises a mot cause map to relate first sensor conditions corresponding to a first fault event of a first component to second sensor conditions corresponding to a second fault event of a second component, the method further comprising, determining, by a root cause processor, a faulty system or group of systems corresponding to the related first and second sensor conditions.

21. The method of claim 12, further comprising partitioning the one or more sensors based at least in part on a statistical dependence among the one or more sensors from a corresponding type of measurement performed.

22. The method of claim 12, further comprising partitioning the one or more sensors by a statistical and dynamical characterization of the one or more fault events.