Methods and systems for detecting deviation of a process variable from expected values

• US 8,606,544 B2
• Filed: 07/25/2006
• Issued: 12/10/2013
• Est. Priority Date: 07/25/2006
• Status: Active Grant

• Current Assignee: Fisher-Rosemount Systems Incorporated (Emerson Electric Company)
• Original Assignee: Fisher-Rosemount Systems Incorporated (Emerson Electric Company)
• Inventors: Miller, John P., Kant, Ravi
• Primary Examiner(s): BHAT, ADITYA S
• Application Number:
  US11/492,460
• Publication Number:
  US 20080052039A1
• Time in Patent Office: 2,695 Days
• Field of Search: 702/33, 702/34, 702/35, 702/182, 702/183, 702/184, 702/185, 702/188, 700/9
• US Class Current: 702/185
• CPC Class Codes: G05B 17/02 electric
• CPC Class Codes: G05B 23/0245 based on a qualitative model, e.g. rule based; if-then decisions

Abstract

In methods and systems that may facilitate detecting abnormal operation in a process plant, values of a process variable are analyzed to determine whether they significantly deviate from expected values. If there is a significant deviation, an indicator may be generated. Analyzing the process variable may include, for example, utilizing a plurality of thresholds determined based on statistics of the process variable. Analyzing the process variable may also include, for example, determining whether a first number of values of the process variable are in a first region, and whether a second number of values are in a second region.

Claims

1. A method, implemented in one or more computer processors, for facilitating detection of abnormal operation of a process in a process plant, comprising:

collecting first data sets for the process while the process is in a first operating region, the first data sets generated from process variables of the process in the first operating region;

generating, in one of the processors, a first regression model of the process in the first operating region using the first data sets;

determining, in one of the processors, a first range in which the first regression model is valid;

generating, in one of the processors, a model of the process to include the first regression model;

collecting second data sets for the process while the process is in a second operating region, the second data sets generated from process variables of the process in the second operating region;

generating, in one of the processors, a second regression model of the process in the second operating region using the second data sets;

determining, in one of the processors, a second range in which the second regression model is valid;

revising, in one of the processors, the model of the process to include the first regression model for the first range and the second regression model for the second range;

receiving, in one of the processors, process variable data, the process variable data generated by a device in a process plant;

receiving, in one of the processors, statistical data regarding the process variable data;

receiving, in one of the processors, at least one parameter associated with at least one threshold based on the received statistical data;

determining at least one threshold based on the received statistical data and the received at least one parameter;

generating, in one of the processors, predicted process variable data using the model of the process;

analyzing, in one of the processors, the received process variable data, the predicted process variable data, and the at least one threshold to determine if the received process variable data significantly deviates from the predicted process variable data; and

generating an indicator of abnormal operation if it is determined that the received process variable data significantly deviates from the predicted process variable data,

wherein analyzing the received process variable data, the predicted process variable data, and the at least one threshold to determine if the received process variable data significantly deviates from the predicted process variable data comprises;

generating a difference signal based on the received process variable data and the predicted process variable data; and

detecting whether the difference signal is increasingly deviating from zero by doing at least one of;

determining, in one of the processors, whether Z_k1>

Z_k2>

. . . >

Z_kB>

X, wherein Z_k1, Z_k2, . . . , Z_kB are at least a subset of B values within a set of A consecutive values of the difference signal at time indexes k1, k2, . . . kB, wherein A is a positive integer greater than 2, B is a positive integer less than or equal to A and greater than 2, wherein X is a first threshold, and wherein k1>

k2 . . . >

kB; and

determining, in one of the processors, whether Z_k1<

Z_k2<

. . . <

Z_kB<

Y, wherein Y is a second threshold.

2. A method according to claim 1, wherein determining the at least one threshold includes determining a first threshold;

wherein analyzing the received process variable data, predicted process variable data, and the at least one threshold comprises determining whether a first number of consecutive values of the received process variable data lie within a first region defined based on the first threshold, the first number greater than one.

3. A method according to claim 2, wherein determining the at least one threshold includes determining a second threshold;

wherein analyzing the received process variable data, predicted process variable data, and the at least one threshold comprises determining whether a second number of consecutive values of the received process variable data lie within a second region defined based on the second threshold, the second number greater than one.

4. A method according to claim 2, wherein determining the at least one threshold includes determining a second threshold;

wherein analyzing the received process variable data, predicted process variable data, and the at least one threshold comprises determining whether the first number of consecutive values of the received process variable data lie within a second region defined based on the second threshold or the first region.

5. A method according to claim 2, wherein receiving statistical data regarding the process variable data comprises receiving a standard deviation regarding the process variable data;

where receiving the at least one parameter comprises receiving an indication of a real number from a configuration application, the real number associated with the first threshold;

wherein determining the at least one threshold comprises determining the first threshold by multiplying the standard deviation with the real number.

6. A method according to claim 5, wherein receiving the at least one parameter comprises receiving the first number from the configuration application.

7. A method according to claim 1, wherein receiving statistical data regarding the process variable data comprises receiving at least one of a standard deviation regarding the process variable data, a variance regarding the process variable data, or a mean regarding the process variable data.

8. A method according to claim 1, further comprising revising at least one of the first range and the second range, if necessary, so that the first range and the second range do not overlap.

9. A method according to claim 1, further comprising:

collecting at least third data sets for the process while the process is in at least a third operating region, the at least third data sets generated from process variables of the process in the at least the third operating region;

generating at least a third regression model of the process in the at least the third operating region using the at least third data sets;

determining at least a third range in which the at least the third regression model is valid; and

revising the model of the process to include the first regression model for the first range, the second regression model for the second range and the at least the third regression model for at least the third range.

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

generating an interpolated model between the first range and the second range; and

revising the model of the process to include the first regression model for the first range, the second regression model for the second range and the interpolated model between the first range and the second range.

11. A tangible, non-transitory medium storing machine readable instructions, the machine readable instructions capable of causing one or more machines to:

collect first data sets for the process while the process is in a first operating region, the first data sets generated from process variables of the process in the first operating region;

generate a first regression model of the process in the first operating region using the first data sets;

determine a first range in which the first regression model is valid;

generate a model of the process to include the first regression model;

collect second data sets for the process while the process is in a second operating region, the second data sets generated from process variables of the process in the second operating region;

generate a second regression model of the process in the second operating region using the second data sets;

determine a second range in which the second regression model is valid;

revise the model of the process to include the first regression model for the first range and the second regression model for the second range;

receive process variable data, the process variable data generated by a device in a process plant;

receive statistical data regarding the process variable data;

receive at least one parameter associated with at least one threshold based on the received statistical data;

determine at least one threshold based on the received statistical data and the received at least one parameter;

generate predicted process variable data using the model of the process;

analyze the received process variable data, the predicted process variable data, and the at least one threshold to determine if the received process variable data significantly deviates from the predicted process variable data; and

generate an indicator of abnormal operation if it is determined that the received process variable data significantly deviates from the predicted process variable data,

wherein the instructions for analyzing the received process variable data, the predicted process variable data, and the at least one threshold to determine if the received process variable data significantly deviates from the predicted process variable data comprise instructions capable of causing the one or more machines to;

generate a difference signal based on the received process variable data and the predicted process variable data; and

detect whether the difference signal is increasingly deviating from zero by doing at least one of;

determine whether Z_k1>

Z_k2>

. . . >

Z_kB>

X, wherein Z_k1, Z_k2, . . . , Z_kB are at least a subset of B values within a set of A consecutive values of the difference signal at time indexes k1, k2, . . . kB, wherein A is a positive integer greater than 2, B is a positive integer less than or equal to A and greater than 2, wherein X is a first threshold, and wherein k1>

k2 . . . >

kB; and

determine whether Z_k1<

Z_k2<

. . . <

Z_kB<

Y, wherein Y is a second threshold.

12. A method, implemented in one or more computer processors, for facilitating detection of abnormal operation of a process in a process plant, comprising:

collecting first data sets for the process while the process is in a first operating region, the first data sets generated from process variables of the process in the first operating region;

generating, in one of the processors, a first regression model of the process in the first operating region using the first data sets;

determining, in one of the processors, a first range in which the first regression model is valid;

generating, in one of the processors, a model of the process to include the first regression model;

collecting second data sets for the process while the process is in a second operating region, the second data sets generated from process variables of the process in the second operating region;

generating, in one of the processors, a second regression model of the process in the second operating region using the second data sets;

determining, in one of the processors, a second range in which the second regression model is valid;

revising, in one of the processors, the model of the process to include the first regression model for the first range and the second regression model for the second range;

receiving, in one of the processors, process variable data associated with a process plant;

generating, in one of the processors, expected process variable data using the model of the process;

determining, in one of the processors, whether a first number of values of the received process variable data are within a first region based on the expected process variable data;

generating an indicator of significant deviation if it is determined that the first number of values of the received process variable data is within the first region;

determining, in one of the processors, whether a second number of values of the received process variable data is within a second region based on the expected process variable data, wherein the second number is greater than the first number, wherein the second region is different than the first region; and

generating the indicator of significant deviation if it is determined that the second number of values of the received process variable data is within the second region,

wherein generating the indicator of significant deviation comprises analyzing a difference signal generated based on received process variable data and the expected process variable data and detecting whether the difference signal is increasingly deviating from zero by doing at least one of;

determining, in one of the processors, whether Z_k1>

Z_k2>

. . . >

Z_kB>

X wherein Z_k1, Z_k2, . . . , Z_kB are at least a subset of B values within a set of A consecutive values of the difference signal at time indexes k1, k2, . . . kB, wherein A is a positive integer greater than 2, B is a positive integer less than or equal to A and greater than 2, wherein X is a first threshold, and wherein k1>

k2 . . . >

kB; and

determining, in one of the processors, whether Z_k1<

Z_k2<

. . . <

Z_kB<

Y, wherein Y is a second threshold.

13. A method according to claim 12, wherein determining whether the first number of values of the received process variable data is within the first region comprises determining if at least the first number of values out of a third number of consecutive values of the received process variable data is within the first region, wherein the third number is greater than the first number;

and

wherein determining whether the second number of values of the received process variable data is within the second region comprises determining if at least the second number of values out of a fourth number of consecutive values of the received process variable data is within the second region, wherein the fourth number is greater than the second number.

14. A method according to claim 12, wherein determining whether the first number of values of the received process variable data is within the first region comprises determining whether the first number of consecutive values of the received process variable data is within the first region;

and

wherein determining whether the second number of values of the received process variable data is within the second region comprises determining whether the second number of consecutive values of the received process variable data is within the second region.

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

determining whether a third number of values of the received process variable data is within a third region based on the expected process variable data, wherein the third number is greater than the second number, wherein the third region is different than the first region and different than the second region; and

generating the indicator of significant deviation if it is determined that the third number of values of the received process variable data is within the third region.

16. A method according to claim 15, wherein the first region comprises values greater than expected values of the received process variable data by at least three standard deviations;

wherein the second region comprises values greater than expected values of the received process variable data by at least two standard deviations; and

wherein the third region comprises values greater than expected values of the received process variable data by at least one standard deviation.

17. A method according to claim 16, wherein the first number is at least one;

wherein the second number is at least two; and

wherein the third number is at least three.

18. A method according to claim 16, further comprising:

determining whether a fourth number of values of the received process variable data is within a fourth region based on the expected process variable data, wherein the fourth number is greater than the third number, wherein the fourth region is different than the first region, the second region, and the third region;

generating the indicator of significant deviation if it is determined that the fourth number of consecutive values of the received data is within the fourth region;

determining whether a fifth number of values of the received process variable data is within a fifth region based on the expected process variable data, wherein the fifth number is greater than the third number, wherein the fifth region is different than the first region, the second region, the third region, and the fourth region; and

generating the indicator of significant deviation if it is determined that the fifth number of consecutive values of the received data is within the fifth region.

19. A method according to claim 18, wherein the fourth region comprises a region including values greater than expected values of the received process variable data;

wherein the fifth region comprises a region including values less than expected values of the received process variable data.

20. A method according to claim 15, wherein the first region comprises values less than expected values of the received process variable data by at least three standard deviations;

wherein the second region comprises values less than expected values of the received process variable data by at least two standard deviations; and

wherein the third region comprises values less than expected values of the received process variable data by at least one standard deviation.

21. A tangible, non-transitory medium storing machine readable instructions, the machine readable instructions capable of causing one or more machines to:

collect first data sets for the process while the process is in a first operating region, the first data sets generated from process variables of the process in the first operating region;

generate a first regression model of the process in the first operating region using the first data sets;

determine a first range in which the first regression model is valid;

generate a model of the process to include the first regression model;

collect second data sets for the process while the process is in a second operating region, the second data sets generated from process variables of the process in the second operating region;

generate a second regression model of the process in the second operating region using the second data sets;

determine a second range in which the second regression model is valid;

revise the model of the process to include the first regression model for the first range and the second regression model for the second range;

receive process variable data associated with a process plant;

generate expected process variable data using the model of the process;

determine whether a first number of values of the received process variable data are within a first region based on the expected process variable data;

generate an indicator of significant deviation if it is determined that the first number of consecutive values of the received process variable data is within the first region;

determine whether a second number of values of the received process variable data is within a second region based on the expected process variable data, wherein the second number is greater than the first number, wherein the second region is different than the first region; and

generate the indicator of significant deviation if it is determined that the second number of consecutive values of the received process variable data is within the second region,

wherein the machine readable instructions operable to cause the one or more processors to generate the indicator of significant deviation comprises instructions operable to cause the one or more processors to analyze a difference signal generated based on received process variable data and the expected process variable data and detect whether the difference signal is increasingly deviating from zero by doing at least one of;

determining whether Z_k1>

Z_k2>

. . . >

Z_kB>

X, wherein Z_k1, Z_k2, . . . , Z_kB are at least a subset of B values within a set of A consecutive values of the difference signal at time indexes k1, k2, . . . kB, wherein A is a positive integer greater than 2, B is a positive integer less than or equal to A and greater than 2, wherein X is a first threshold, and wherein k1>

k2 . . . >

kB; and

determining whether Z_k1<

Z_k2<

. . . <

Z_kB<

Y, wherein Y is a second threshold.

22. A system for facilitating detection of abnormal operation of a process in a process plant, comprising:

a configurable model of the process in the process plant, the configurable model including a first regression model in a first range corresponding to a first operating region of the process, the configurable model capable of being subsequently configured to include a second regression model in a second range corresponding to a second operating region different than the first operating region;

the system further comprising a deviation detector coupled to the configurable model, the deviation detector configured to determine if the process significantly deviates from an output of the model

a threshold generator to receive process variable statistical data and to generate a plurality of thresholds based on the process variable statistical data;

a comparator coupled to the configurable model, the comparator configured to;

receive process variable data and the plurality of thresholds,

determine whether a first number of values of the received process variable data are within a first region based on a first threshold of the plurality of thresholds and based on an output of the configurable model,

generate an indicator of significant deviation if it is determined that the first number of values of the received process variable data is within the first region,

determine whether a second number of values of the received process variable data is within a second region based on a second threshold of the plurality of thresholds and based on the output of the configurable model, wherein the second number is greater than the first number, wherein the second region is different than the first region, and

generate the indicator of significant deviation if it is determined that the second number of values of the received process variable data is within the second region,

wherein generating the indicator of significant deviation comprises analyzing a difference signal generated based on received process variable data and the expected process variable data and detecting whether the difference signal is increasingly deviating from zero by doing at least one of;

determining whether Z_k1>

Z_k2>

. . . >

Z_kB>

X, wherein Z_k1, Z_k2, . . . , Z_kB are at least a subset of B values within a set of A consecutive values of the difference signal at time indexes k1, k2, . . . kB, wherein A is a positive integer greater than 2, B is a positive integer less than or equal to A and greater than 2, wherein X is a first threshold, and wherein k1>

k2 . . . >

kB; and

determining whether Z_k1<

Z_k2<

. . . <

Z_kB<

Y, wherein Y is a second threshold.

23. A system according to claim 22, wherein the process variable statistical data includes a standard deviation;

wherein the first threshold is a first multiple of the standard deviation and wherein the second threshold is a second multiple of the standard deviation;

wherein the first region comprises values greater than corresponding values of the output of the configurable model plus the first threshold; and

wherein the second region comprises values greater than corresponding values of the output of the configurable model plus the second threshold.

24. A system according to claim 23, wherein the comparator is further configured to:

determine whether a third number of values of the received process variable data is within a third region based on a third threshold of the plurality of thresholds and based on the output of the configurable model, wherein the third threshold is a third multiple of the standard deviation, wherein the third number is greater than the second number, wherein the third region is different than the first region and different than the second region; and

generate the indicator of significant deviation if it is determined that the third number of values of the received process variable data is within the third region.

25. A system according to claim 24, wherein the first number is at least one;

wherein the second number is at least two;

wherein the third number is at least three;

wherein the second multiple is greater than the first multiple; and

wherein the third multiple is greater than the second multiple.

26. A system according to claim 22, wherein the configurable model is capable of being configured, after being configured to include the second regression model, to include at least a third regression model in at least a third range corresponding to at least a third operating region different than the first operating region and the second operating region.

27. A system according to claim 22, wherein the configurable model is capable of generating an interpolated model between the first range and the second range.

28. A system according to claim 27, wherein the configurable model is capable of generating a prediction of a Y value as a function of an X value, wherein the Y value is generated from a dependent process variable and the X value is generated from an independent process variable;

wherein, after the configuration model is configured to include the second regression model and after generating the interpolation model, the configurable model is capable of;

generating the prediction of the Y value using the first regression model if the X value is in the first range,

generating the prediction of the Y value using the second regression model if the X value is in the second range, and

generating the prediction of the Y value using the interpolation model if the X value is between the first range and the second range.