Method and System for Modeling Behavior in a Process Plant

• US 20080177513A1
• Filed: 01/04/2007
• Published: 07/24/2008
• Est. Priority Date: 01/04/2007
• 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.
• Granted Patent: US 8,032,340 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G05B 17/02 electric
• 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

Abstract

In a method for generating a model for modeling at least a portion of the process plant, M process variable data sets, where M is an integer, may be used to determine statistical data that may be utilized to scale process variable data sets. The M process variable data sets are scaled and then utilized to calculate intermediate model terms. For each additional process variable data set, it is scaled using the statistical data and then utilized to update the intermediate model terms. When an adequate number of process variable data sets have been processed, the model may be calculated using the intermediate model terms.

Claims

1. A method for modeling behavior in a process plant, comprising:

receiving M process variable data sets, wherein M is an integer;

calculating statistical data for process variables associated with the M process variable data sets using the M process variable data sets and not using additional process variable data sets;

scaling the M process variable data sets using the statistical data;

calculating a plurality of intermediate modeling terms using the scaled M process variable data sets;

receiving the additional process variable data sets associated with the process variables;

scaling the additional process variable data sets using the statistical data;

after calculating the plurality of intermediate modeling terms using the scaled M process variable data sets, updating the plurality of intermediate modeling terms using the scaled additional process variable data sets; and

calculating a model of the process variable using the plurality of intermediate modeling terms.

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

before calculating the plurality of intermediate modeling terms using the scaled M process variable data sets, initializing the plurality of intermediate modeling terms;

before calculating the plurality of intermediate modeling terms using the scaled M process variable data sets, updating the plurality of intermediate modeling terms using the process variable data sets; and

replacing the plurality of intermediate modeling terms updated using the M process variable data sets with the plurality of intermediate modeling terms calculated using the scaled M process variable data sets.

3. A method according to claim 1, wherein updating the plurality of intermediate modeling terms using the scaled additional process variable data sets comprises updating the plurality of intermediate modeling terms for each scaled additional process variable data set.

4. A method according to claim 1, wherein calculating the model comprises calculating a regression model.

5. A method according to claim 4, wherein calculating the regression model comprises calculating a linear regression model.

6. A method according to claim 5, wherein calculating the plurality of intermediate modeling terms using the scaled M process variable data sets comprises calculating a plurality of sums of powers of the process variables.

7. A method according to claim 6, wherein calculating the statistical data comprises:

calculating means of the process variables using the M process variable data sets; and

calculating standard deviations of the process variables using the M process variable data sets.

8. A method according to claim 7, wherein scaling the M process variable data sets comprises, for each of the M process variable data sets:

subtracting the corresponding mean from each component of the process variable data set; and

subsequent to subtracting the corresponding mean from each component of the process variable data set, dividing each component of the process variable data set by the corresponding standard deviation.

9. A method according to claim 8, wherein scaling the additional process variable data sets, comprises, for each of the additional process variable data sets:

subtracting the corresponding mean from each component of the process variable data set; and

subsequent to subtracting the corresponding mean from each component of the process variable data set, dividing each component of the process variable data set by the corresponding standard deviation.

10. A method according to claim 4, wherein calculating the regression model comprises calculating a non-linear regression model.

11. A method according to claim 1, wherein receiving M process variable data sets comprises receiving at least one of process variable values, filtered process variable values, or statistical data generated from process variable values;

and

wherein receiving the additional process variable data sets comprises receiving at least one of process variable values, filtered process variable values, or statistical data generated from process variable values.

12. A method according to claim 1, further comprising monitoring the process variables for abnormal operation using the model.

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

determining whether a process variable significantly deviates from the model; and

generating an indicator of abnormal operation if it is determined that the process variable significantly deviates from the model.

14. A method according to claim 1, wherein receiving M process variable data sets comprises receiving data via a communication network;

and

wherein receiving the additional process variable data sets comprises receiving data via the communication network.

15. A method according to claim 1, wherein calculating the model of the process variable comprises calculating a model of the process variable as a function of at least one other process variable.

16. A method according to claim 1, wherein calculating the model of the process variable comprises calculating a model of a first plurality of process variables as a function of a second plurality of process variables.

17. A system for modeling behavior in a process plant, comprising:

a memory;

a processor coupled to the memory, the processor configured according to machine readable instructions stored in the memory to;

calculate statistical data for process variables associated with M process variable data sets using the M process variable data sets and not using additional process variable data sets, the M process variable data sets stored in the memory, wherein M is an integer,

scale the M process variable data sets using the statistical data,

calculate a plurality of intermediate modeling terms using the scaled M process variable data sets,

scale additional process variable data sets using the statistical data,

after calculating the plurality of intermediate modeling terms using the scaled M process variable data sets, update the plurality of intermediate modeling terms using the scaled additional process variable data sets, and

calculate a model of the process variable using the plurality of intermediate modeling terms.

18. A system according to claim 17, wherein the processor is further configured according to machine readable instructions stored in the memory to monitor the process variables for abnormal operation using the model.

19. A system according to claim 18, wherein the processor is further configured according to machine readable instructions stored in the memory to:

determine whether a process variable significantly deviates from the model, and

generate an indicator of abnormal operation if it is determined that the process variable significantly deviates from the model.

20. A tangible medium storing machine readable instructions, the machine readable instructions capable of causing one or more machines to:

calculate statistical data for process variables associated with M process variable data sets using the M process variable data sets and not using additional process variable data sets, the M process variable data sets stored in the memory, wherein M is an integer;

scale the M process variable data sets using the statistical data;

calculate a plurality of intermediate modeling terms using the scaled M process variable data sets;

scale additional process variable data sets using the statistical data;

after calculating the plurality of intermediate modeling terms using the scaled M process variable data sets, update the plurality of intermediate modeling terms using the scaled additional process variable data sets; and

calculate a model of the process variable using the plurality of intermediate modeling terms.

21. A tangible medium storing machine readable instructions according to claim 20, the machine readable instructions further capable of causing one or more machines to monitor the process variables for abnormal operation using the model.

22. A tangible medium storing machine readable instructions according to claim 21, the machine readable instructions further capable of causing one or more machines to:

determine whether a process variable significantly deviates from the model, and

generate an indicator of abnormal operation if it is determined that the process variable significantly deviates from the model.

23. A system for detecting an abnormal operation in a process plant, comprising:

a configurable model of a process in the process plant, wherein the configurable model;

receives M process variable data sets, wherein M is an integer,

calculates statistical data for process variables associated with the M process variable data sets using the M process variable data sets and not using additional process variable data sets,

scales the M process variable data sets using the statistical data,

calculates a plurality of intermediate modeling terms using the scaled M process variable data sets,

receives the additional process variable data sets associated with the process variables,

scales the additional process variable data sets using the statistical data,

after calculating the plurality of intermediate modeling terms using the scaled M process variable data sets, updates the plurality of intermediate modeling terms using the scaled additional process variable data sets, and

calculates a model of the process variable using the plurality of intermediate modeling terms;

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.