Statistical signatures used with multivariate analysis for steady-state detection in a process

• US 7,853,339 B2
• Filed: 09/28/2007
• Issued: 12/14/2010
• Est. Priority Date: 09/29/2006
• Status: Active Grant

• Current Assignee: Fisher-Rosemount Systems Incorporated (Emerson Electric Company)
• Original Assignee: Fisher-Rosemount Systems Incorporated (Emerson Electric Company)
• Inventors: Lundeberg, Marcus R., Miller, John P.
• Primary Examiner(s): Patel; Ramesh B
• Application Number:
  US11/864,505
• Publication Number:
  US 20080082181A1
• Time in Patent Office: 1,173 Days
• Field of Search: 700/28, 700/50, 700/51, 700/52, 700/53, 700/54, 702/179, 702/180, 702/181
• US Class Current: 700/51
• CPC Class Codes: G05B 23/021 adopting a different treatment of each operating region or a different mode of the monitored system, e.g. transient modes; different operating configurations of monitored system
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• CPC Class Codes: G05B 23/0245 based on a qualitative model, e.g. rule based; if-then decisions
• CPC Class Codes: Y10S 715/965 for process control and configuration

Abstract

Methods and systems to detect steady-state operations in a process of a process plant include collecting process data. The collected process data is generated from a plurality of process variables of the process. A multivariate statistical model of the operation of the process is generated using the process data. The multivariate statistical model may be generated from a principal component analysis. The model is executed to generate outputs corresponding to the most significant variations in the process. Statistical measures of the outputs are generated and used to determine whether a steady-state or unsteady-state is related to the process.

Claims

1. A system for facilitating detection of a steady-state operation of a process in a process plant, the system comprising:

a data collection tool adapted to collect process data from a process within the process plant, wherein the collected process data is representative of an operation of the process and wherein the collected process data is generated from a plurality of process variables;

an analysis tool comprising a multivariate statistical analysis engine adapted to generate a representation of the operation of the process based on a set of the collected process data representative of the operation of the process, wherein the analysis tool is adapted to execute the representation based on the set of the collected process data to generate a first outcome corresponding to the largest amount of variation in the process; and

a statistical calculation tool adapted to perform a statistical calculation on the first outcome corresponding to the largest amount of variation in the process to generate a statistical measure of the first outcome, and adapted to determine a steady-state operation status related to the process based on the statistical measure.

2. The system of claim 1, wherein the analysis tool is adapted to execute the representation based on the set of the collected process data representative of the operation of the process to generate a second outcome corresponding to a significant amount of variation in the process different from the largest amount of variation in the process.

3. The system of claim 2, wherein the statistical calculation tool comprises a first and second statistical calculation tool, wherein the first statistical calculation tool is adapted to perform a statistical calculation on the first outcome to generate a statistical measure of the first outcome and the second statistical calculation tool is adapted to perform a statistical calculation on the second outcome generate a statistical measure of the second outcome, and wherein the first and second statistical calculation tools are each adapted to determine the steady-state operation status related to the process based on the corresponding statistical measures.

4. The system of claim 3, wherein the first and second statistical calculation tools are each adapted to generate an indication pertaining to the corresponding determination of a steady-state operation status related to the process, the system further comprising a monitoring tool adapted to analyze each of the steady-state indications of the first and second calculation tools and adapted to determine the steady-state operation status of the process based on any one of the steady-state indications generated by the first and second calculation tools.

5. The system of claim 1, wherein the statistical calculation tool is adapted to perform a statistical calculation on the first outcome to generate a first mean and a first standard deviation of the first outcome corresponding to a first sampling window from the set of collected process data and adapted to generate a second mean and a second standard deviation of the first outcome corresponding to a second sampling window from the set of collected process data.

6. The system of claim 5, wherein the statistical calculation tool is adapted to determine the steady-state operation status related to the process based on a difference between the first and second mean as compared to at least one of the first or second standard deviations.

7. The system of claim 6, wherein the statistical calculation tool is adapted to determine the steady-state operation status related to the process based on the difference between the first and second mean as compared to the minimum of the first and second standard deviations.

8. The system of claim 1, wherein the multivariate statistical analysis engine is adapted to perform a principal component analysis to generate the representation of the operation of the process based on the set of the collected process data representative of the operation of the process, wherein the first outcome corresponds to the most significant component generated from the principal component analysis.

9. A method of facilitating detection of a steady-state operation of a process in a process plant, the method comprising:

collecting process data from a process within the process plant, wherein the collected process data is representative of an operation of the process and wherein the collected process data is generated from a plurality of process variables;

generating a multivariate statistical representation of the operation of the process based on a set of the collected process data representative of the operation of the process;

generating a first outcome corresponding to the largest amount of variation in the process from the multivariate statistical representation;

generating a statistical measure of the first outcome; and

determining the presence of a steady-state operation status related to the process based on the first statistical measure.

10. The method of claim 9, further comprising generating a second outcome corresponding to a significant amount of variation in the process from the multivariate statistical representation, wherein the significant amount of variation in the process is different from the largest amount of variation in the process.

11. The method of claim 10, further comprising generating a statistical measure of the second outcome, wherein determining the presence of a steady-state operation related to the process comprises determining the presence of a steady-state operation status related to the process based on the statistical measures of the first and second outcomes.

12. The method of claim 11, further comprising:

generating a first indication pertaining to the determination of a steady-state operation status based on the statistical measure of the first outcome; and

generating a second indication pertaining to the determination of a steady-state operation status based on the statistical measure of the second outcome;

wherein determining the presence of a steady-state operation status related to the process comprises determining the presence of a steady-state operation status related to the process based on any one of the first and second steady-state indications.

13. The method of claim 9, wherein generating a statistical measure of the first outcome comprises:

generating a first mean and a first standard deviation of the first outcome corresponding to a first sampling window from the set of collected process data; and

generating a second mean and a second standard deviation of the first outcome corresponding to a second sampling window from the set of collected process data.

14. The method of claim 13, wherein determining the presence of a steady-state operation status related to the process comprises determining the presence of a steady-state operation status related to the process based on a difference between the first and second mean as compared to at least one of the first or second standard deviations.

15. The method of claim 14, wherein determining the presence of a steady-state operation status related to the process comprises determining the presence of a steady-state operation status related to the process based on the difference between the first and second mean as compared to the minimum of the first and second standard deviations.

16. The method of claim 9, wherein generating a multivariate statistical representation of the operation of the process comprises performing a principal component analysis to generate a representation of the operation of the process based on the set of the collected process data representative of the operation of the process, and wherein generating a first outcome comprises generating a first outcome corresponding to the most significant component generated from the principal component analysis.

17. A method of facilitating detection of a steady-state operation of a process in a process plant, the method comprising:

collecting process data from a process within the process plant, wherein the collected process data is representative of an operation of the process and wherein the collected process data is generated from a plurality of process variables of the process comprising a first data space having a plurality of dimensions;

generating a model of the operation of the process using a set of the collected process data of the process, wherein the model comprises a measure of the operation of the process when the process is operating at different times within a second data space having fewer dimensions than the first data space;

generating a plurality of outputs from the model of the operation of the process, each output corresponding to a different significant variation in the process;

generating a statistical measure for each of the plurality of outputs; and

determining the presence of a steady-state operation status of the process based on any one of the plurality of statistical measures.

18. The method of claim 17, wherein generating a model comprises performing a principal component analysis on the set of the collected process data to represent the operation of the process in a loading matrix defining a subspace of the first data space, wherein each output corresponds to a different significant component generated from the principal component analysis.

19. The method of claim 17, wherein generating a statistical measure for each of the plurality of outputs comprises generating a first and second mean and a first and second standard deviation for each of the plurality of outputs, wherein the first mean and first standard deviation correspond to a first sampling window from the set of collected process data and the second mean and second standard deviation correspond to a second sampling window from the set of collected process data, and wherein determining the presence of a steady-state operation status of the process comprises determining the presence of a steady-state operation status of the process based on a difference between the first and second mean as compared to at least one of the first or second standard deviations for any one of the plurality of outputs.

20. The method of claim 19, wherein determining the presence of a steady-state operation status of the process comprises determining the presence of a steady-state operation status of the process based on a difference between the first and second mean as compared to the minimum of the first and second standard deviations for any one of the plurality of outputs.

21. A system for facilitating detection of a steady-state operation of a process in a process plant, the system comprising:

a data collection tool adapted to collect process data from a process within the process plant, wherein the collected process data is representative of an operation of the process wherein the collected process data is generated from a plurality of process variables of the process comprising a first data space having a plurality of dimensions;

an analysis tool adapted to generate a model of the operation of the process based on a set of the collected process data of the process, wherein the model comprises a measure of the operation of the process when the process is operating at different times within a second data space having fewer dimensions than the first data space, and wherein the analysis tool is adapted to execute the model of the operation of the process to generate a plurality of outputs each corresponding to a different significant variation in the process; and

a statistical calculation tool adapted to perform a statistical calculation on each of the plurality of outputs corresponding to a different significant variation in the process to generate a statistical measure for each of the plurality of outputs, and adapted to determine a steady-state operation status related to the process based on any one of the plurality of statistical measures.

22. The system of claim 21, wherein the analysis tool comprises a multivariate statistical engine adapted to perform a principal component analysis on the set of the collected process data to represent the operation of the process in a loading matrix defining a subspace of the first data space, wherein each output corresponds to a different significant component generated from the principal component analysis.

23. The system of claim 21, wherein the statistical calculation tool is adapted to perform a statistical calculation on each of the plurality of outputs to generate a first and second mean and a first and second standard deviation for each of the plurality of outputs, wherein the first mean and first standard deviation correspond to a first sampling window from the set of collected process data and the second mean and second standard deviation correspond to a second sampling window from the set of collected process data, and wherein the statistical calculation tool is adapted to determine the presence of a steady-state operation status of the process based on a difference between the first and second mean as compared to at least one of the first or second standard deviations for any one of the plurality of outputs.

24. The system of claim 23, wherein the statistical calculation tool is adapted to determine the steady-state operation status of the process based on a difference between the first and second mean as compared to the minimum of the first and second standard deviations for any one of the plurality of outputs.