System and method for detecting an abnormal situation associated with a process gain of a control loop
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G06F-011/30
G21C-017/00
출원번호
UP-0149001
(2005-06-09)
등록번호
US-7660701
(2010-04-02)
발명자
/ 주소
Sharpe, Jr., Joseph H.
출원인 / 주소
Fisher-Rosemount Systems, Inc.
대리인 / 주소
Marshall, Gerstein & Borun
인용정보
피인용 횟수 :
9인용 특허 :
156
초록▼
In a method for monitoring a control loop in a process plant, process gain data associated with a control loop may be collected. The collected process gain data may be used to determine an expected process gain behavior. For example, expected values of a process variable for given values of a load v
In a method for monitoring a control loop in a process plant, process gain data associated with a control loop may be collected. The collected process gain data may be used to determine an expected process gain behavior. For example, expected values of a process variable for given values of a load variable may be determined. As another example, expected changes in a process variable for given changes in a load variable may be determined. Then, during operation of the control loop, the process gain may be monitored. If the monitored process gain substantially deviates from the expected behavior, this may indicate an abnormal situation associated with the control loop.
대표청구항▼
The invention claimed is: 1. A method for monitoring operation of a control loop in a process plant, comprising: collecting, at one or more of a plurality of computing devices, process gain data associated with a first operating region of a control loop in a process plant, the control loop associat
The invention claimed is: 1. A method for monitoring operation of a control loop in a process plant, comprising: collecting, at one or more of a plurality of computing devices, process gain data associated with a first operating region of a control loop in a process plant, the control loop associated with a unit operation in the process plant, wherein a load variable varies in the first operating region; determining, at one of the plurality of computing devices, an expected process gain behavior in the first operating region based on the collected process gain data, wherein an expected value of the process gain varies as the load variable varies; wherein determining the expected process gain behavior in the first operating region comprises fitting a curve to the collected process gain data, and wherein the curve corresponds to the expected process gain behavior in the first operating region; monitoring, at one of the plurality of computing devices, the process gain during operation of the control loop in the first operating region; determining, at one of the plurality of computing devices, when the monitored process gain substantially deviates from the curve corresponding to the expected process gain behavior in the first operating region; determining, at one of the plurality of computing devices, an abnormal situation associated with at least one of the control loop or the unit operation based at least on a substantial deviation from the curve corresponding to the expected process gain behavior in the first operating region; and generating, at one of the plurality of computing devices, an abnormal situation indicator when an abnormal situation is determined. 2. A method according to claim 1, wherein collecting process gain data associated with the first operating region of the control loop comprises at least one of: collecting data regarding a process variable versus the load variable; collecting data regarding the load variable versus the process variable; collecting data regarding a process gain versus the load variable; collecting data regarding the load variable versus a process gain; or collecting data regarding the process variable versus one or more other process variables. 3. A method according to claim 2, wherein the load variable comprises at least one of a control output or another process variable. 4. A method according to claim 1, wherein determining the expected process gain behavior in the first operating region comprises at least one of: determining expected values of a variable; or determining expected rates of change of the variable. 5. A method according to claim 1, wherein determining the abnormal situation comprises determining an abnormal situation associated with the unit operation based at least on a substantial deviation from the curve corresponding to the expected process gain behavior in the first operating region of the control loop and a substantial deviation from an expected process gain behavior of a different control loop associated with the unit operation. 6. A method according to claim 1, further comprising: collecting, at one of the plurality of computing devices, process gain data associated with at least a second operating region of the control loop in the process plant; determining, at one of the plurality of computing devices, an expected process gain behavior in the at least the second operating region based on the collected process gain data; wherein determining the expected process gain behavior in the at least the second operating region comprises fitting another curve to the collected process gain data associated with the at least a second operating region, and wherein the other curve corresponds to the expected process gain behavior in the at least the second operating region; monitoring, at one of the plurality of computing devices, the process gain during operation of the control loop in the at least the second operating region; determining, at one of the plurality of computing devices, when the monitored process gain substantially deviates from the curve corresponding to the expected process gain behavior in the at least the second operating region; and wherein determining the abnormal situation comprises determining the abnormal situation based at least on a substantial deviation from the curve corresponding to the expected process gain behavior in the first operating region or a substantial deviation from the curve corresponding to the expected process gain behavior in the second operating region. 7. A method according to claim 1, wherein collecting process gain data associated with the first operating region of the control loop comprises collecting process gain data regarding a plurality of process gains associated with the control loop, the plurality of process gains including at least a first process gain and a second process gain; wherein determining the expected process gain behavior in the first operating region comprises determining an expected behavior of the first process gain with respect to at least the second process gain; wherein the curve corresponding to the expected process gain behavior in the first operating region corresponds to the expected behavior of the first process gain with respect to at least the second process gain; wherein monitoring the process gain during operation of the control loop in the first operating region comprises monitoring the first process gain and monitoring the second process gain; wherein determining when the monitored process gain substantially deviates from the curve corresponding to the expected process gain behavior in the first operating region comprises determining when the monitored first process gain substantially deviates from the expected behavior of the first process gain with respect to at least the second process gain. 8. A method according to claim 1, wherein determining when the monitored process gain substantially deviates from the curve corresponding to the expected process gain behavior in the first operating region comprises at least one of: determining when the monitored process gain is below the curve corresponding to the expected process gain behavior in the first operating region for a specified period of time; or determining when the monitored process gain is above the curve corresponding to the expected process gain behavior in the first operating region for the specified period of time. 9. A method according to claim 1, wherein determining the expected process gain behavior in the first operating region comprises determining a confidence interval for the first operating region; wherein determining when the monitored process gain substantially deviates from the expected process gain behavior in the first operating region comprises determining at least when the monitored process gain is outside of the confidence interval in the first operating region. 10. A method according to claim 9, wherein determining when the monitored process gain substantially deviates from the expected process gain behavior in the first operating region comprises determining when the monitored process gain is outside of the confidence interval in the first operating region for a specified period of time. 11. A method according to claim 1, if an abnormal situation is determined, further comprising at least one of: adjusting, at one of the plurality of computing devices, a control parameter associated with the control loop; initiating, at one of the plurality of computing devices, a diagnostic procedure; or shutting down, via one or more computing devices, equipment associated with the control loop. 12. A method according to claim 1, wherein generating an abnormal situation indicator comprises generating an alert. 13. A method according to claim 1, wherein determining the abnormal situation comprises determining at least one of: if the abnormal situation has occurred, or if the abnormal situation will likely occur in the future. 14. A method according to claim 1, further comprising: determining, at one of the plurality of computing devices, when the control loop is operating in a second operating region for which process gain data has not yet been collected; and collecting, at one of the plurality of computing devices, process gain data associated with the second operating region of the control loop in the process plant after determining that the control loop is operating in the second operating region of the control loop; monitoring, at one of the plurality of computing devices, the process gain during operation of the control loop in the second operating region; and determining, at one of the plurality of computing devices, when the monitored process gain substantially deviates from the expected process gain behavior in the second operating region. 15. A method according to claim 14, further comprising; prompting, at one of the plurality of computing devices, an operator whether to collect process gain data associated with the second operating region of the control loop in the process plant after determining that the control loop is operating the second operating region of the control loop; wherein collecting process gain data associated with the second operating region of the control loop comprises process gain data associated with the second operating region if the operator indicates that process gain data associated with the second operating region of the control loop should be collected. 16. A method according to claim 14, wherein a unit of the process plant comprises the control loop; wherein determining when the control loop is operating in the second operating region comprises determining when the unit of the process plant is operating in an operating region for which process gain data associated with the unit has not yet been collected. 17. A tangible medium having stored thereon machine executable instructions, the machine executable instructions capable of causing one or more machines to: collect process gain data associated with a first operating region of a control loop in a process plant, wherein a load variable varies in the first operating region; determine an expected process gain behavior in the first operating region based on the collected process gain data, wherein an expected value of the process gain varies as the load variable varies; wherein determining the expected process gain behavior in the first operating region comprises fitting a curve to the collected process gain data, and wherein the curve corresponds to the expected process gain behavior in the first operating region; monitor the process gain during operation of the control loop in the first operating region; determine when the monitored process gain substantially deviates from the curve corresponding to the expected process gain behavior in the first operating region; determine an abnormal situation associated with at least one of the control loop or the unit operation based at least on a substantial deviation from the curve corresponding to the expected process gain behavior in the first operating region; and generate an abnormal situation indicator if the abnormal situation is determined. 18. A method for monitoring operation of a control loop in a process plant, comprising: collecting process gain data associated with a first operating region of a control loop in a process plant, wherein a load variable varies in the first operating region; determining an expected process gain behavior in the first operating region based on the collected process gain data associated with the first operating region, wherein an expected value of the process gain varies as the load variable varies; wherein determining the expected process gain behavior in the first operating region comprises fitting a curve to the collected process gain data, and wherein the curve corresponds to the expected process gain behavior in the first operating region; providing data indicative of the curve corresponding to the expected process gain behavior in the first operating region to an expert engine; providing process gain data associated with the control loop during operation of the control loop to the expert engine; utilizing the expert engine to detect an abnormal situation associated with the control loop based on the data indicative of the curve corresponding to the expected process gain behavior in the first operating region and the process gain data associated with the control loop during operation of the control loop; and generating an abnormal situation indicator if the abnormal situation is determined. 19. A method according to claim 18, wherein utilizing the expert engine to detect the abnormal situation associated with the control loop comprises determining whether the process gain in the first operating region substantially deviates from the curve corresponding to the expected process gain behavior in the first operating region. 20. A method according to claim 18, further comprising: providing process variable statistical data associated with the control loop during operation of the control loop to the expert engine; and wherein utilizing the expert engine to detect the abnormal situation associated with the control loop comprises utilizing the expert engine to detect the abnormal situation associated with the control loop further based on the process variable statistical data associated with the control loop. 21. A method according to claim 20, wherein the process variable statistical data associated with the control loop comprises statistical data generated by field devices associated with the control loop. 22. A system for monitoring operation of a control loop in a process plant, the system comprising: a process gain signature generator configured to generate a signature of expected process gain behavior associated with a control loop in a process plant, wherein the signature is indicative of a gain of a process variable of the control loop versus a load variable of the control loop, and wherein the gain of the process variable versus the load variable is expected to vary as the load variable varies; wherein the process gain signature generator is configured to fit a curve to collected process gain data, and wherein the signature of expected process gain behavior comprises the curve; a process gain evaluator configured to determine if an actual process gain substantially deviates from the curve; and an abnormal situation detector configured to detect an abnormal situation associated with a process unit associated with the control loop based at least in part on whether the actual process gain substantially deviates from the curve and to generate an abnormal situation indicator if the abnormal situation is detected. 23. A system according to claim 22, further comprising: an interval generator configured to generate an interval associated with the signature of expected process gain behavior; wherein the process gain evaluator is configured to determine if the actual process gain substantially deviates from the curve based on the interval associated with the signature of expected process gain behavior. 24. A system according to claim 22, further comprising an expert system, wherein the expert system comprises at least one of the process gain evaluator or the abnormal situation detector. 25. A system according to claim 24, wherein the expert system is configured to detect an abnormal situation associated with a process unit associated with the control loop based at least in part on whether the actual process gain substantially deviates from the curve. 26. A system according to claim 22, further comprising a process gain data collector configured to collect data to be used by the process gain signature generator to generate the signature of expected process gain behavior associated with the control loop. 27. A system according to claim 22, wherein the abnormal situation detector is configured to detect at least one of: if the abnormal situation has occurred, or if the abnormal situation will likely occur in the future. 28. A method for facilitating monitoring operation of at least a portion of a process plant, comprising: collecting, at one of a plurality of computing devices, process gain data indicative of respective process gains associated with respective unit operations in a process plant, wherein the respective process gains are associated with an operating region, wherein respective load variables vary in the operating region; determining, at one of the plurality of computing devices, expected process gains associated with respective unit operations based on the collected process gain data, wherein expected values of the respective process gains vary as the respective load variables vary; wherein determining the expected process gains associated with respective unit operations comprises fitting respective curves to the collected process gain data, and wherein the respective curves correspond to respective unit operations; providing, at one of the plurality of computing devices, a common set of criteria for determining for each unit operation an abnormal situation associated with the unit operation based at least on whether the process gain associated with the unit operation substantially deviates from the curve associated with the unit operation; permitting, at one of the plurality of computing devices, a user to modify the common set of criteria for a particular unit operation to generate a modified set of criteria; utilizing, at one of the plurality of computing devices, the modified set of criteria to determine for the particular unit operation an abnormal situation associated with the particular unit operation; utilizing, at one of the plurality of computing devices, the common set of criteria to determine for each of at least one other unit operation an abnormal situation associated with the other unit operation; and generating, at one of the plurality of computing devices, an abnormal situation indicator when an abnormal situation for a unit operation is determined. 29. A method according to claim 28, wherein the common set of criteria comprises expert rules to be applied by an expert engine. 30. A method for monitoring operation of a control loop in a process plant, comprising: collecting, at one of the plurality of computing devices, process gain data associated with an operating region of a control loop in a process plant, the control loop associated with a unit operation in the process plant, wherein the process gain data is indicative of a gain of a process variable of the control loop versus a load variable of the control loop, and wherein the gain of the process variable versus the load variable varies as the load variable varies; determining, at one of the plurality of computing devices, an expected process gain behavior in the operating region based on the collected process gain data, wherein the process gain is expected to vary as the load variable varies; wherein determining the expected process gain behavior in the operating region comprises fitting a curve to the collected process gain data, and wherein the curve corresponds to the expected process gain behavior in the operating region; monitoring, at one of the plurality of computing devices, the process gain during operation of the control loop in the operating region; determining, at one of the plurality of computing devices, when the monitored process gain substantially deviates from the curve; determining, at one of the plurality of computing devices, an abnormal situation associated with at least one of the control loop or the unit operation based at least on a substantial deviation from the curve; and generating, at one of the plurality of computing devices, an abnormal situation indicator when an abnormal situation is determined. 31. A method for facilitating monitoring operation of at least a portion of a process plant, comprising: collecting, at one of a plurality of computing devices, process gain data indicative of process gains associated with respective unit operations in a process plant, wherein each of the process gains is a gain of a process variable of the respective unit versus a load variable of the respective unit, and wherein each of the process gains varies as the respective load variable varies; determining, at one of the plurality of computing devices, expected process gains associated with respective unit operations based on the collected process gain data, wherein each of the expected process gains varies as the respective load variable varies; and wherein determining the expected process gains associated with respective unit operations comprises fitting respective curves to the collected process gain data, and wherein the respective curves correspond to respective unit operations; providing, at one of the plurality of computing devices, a common set of criteria for determining for each unit operation an abnormal situation associated with the unit operation based at least on whether the process gain associated with the unit operation substantially deviates from the curve associated with the unit operation.
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Holley Steven R. (Phoenix AZ) Cunningham David C. (Carefree AZ) Kral Kevin D. (Streamwood IL), Apparatus and method for minimizing limit cycle using complementary filtering techniques.
Allison Bruce J. (Vancouver CAX) Ciarniello Joe E. (Coquitlam CAX) Dumont Guy A. (Vancouver CAX) Tessier Patrick J. (North Vancouver CAX), Automatic refiner load control.
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Dundics Marton J. (2448 Holly Ave. ; Suite 200 Annapolis MD 21401) Bradlee Robert S. (2551 Eltham Ave. ; Suite L Norfolk VA 23513), Computer monitoring and testing of automatic control system.
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Rylander Karen S. (Minneapolis MN) Fant Karl M. (Minneapolis MN) Egli Werner H. (Maple Grove MN), Data projection system with compensation for nonplanar screen.
Hobson Daniel E. (Yokon OK) Felts Wayne L. (Oklahoma City OK) Hampshire Randall D. (Edmond OK), Detection of mechanical defects in a disc drive using injected test signals.
Aggers John R. (Apple Valley MN) Brindle Ralph C. (Minnetonka MN) Kidder Kenneth B. (Coon Rapids MN) Ullestad David C. (Brooklyn Park MN), Distributed environmental/load control system.
Call William L. ; Clawson Laurence A. ; Connolly Paul S. ; Freimark Ronald J. ; Gustin Jay W. ; Hodge Michael L. ; McGaugh Paul ; Moore Donald W. ; Rachlin Elliott H. ; Ramsdell Steven C., Emulator for visual display object files and method of operation thereof.
Kline ; Jr. Robert C. (Glendale AZ) Clements Emory C. (Glendale AZ) Krehbiel Gretchen L. (Phoenix AZ) Tanner Darrell R. (Cincinnati OH) Strilich James A. (Phoenix AZ) Chappell David A. (West Chester , Flexible method for building a recipe in a process control system.
Lake Harold ; Prentice David P. ; Greenup John ; Piper Charles ; Korowitz Simon, Industrial field controlling device with controller and expansion modules.
Bob Spriggs ; Bob Hayashida ; Ken Ceglia ; Diana Seymour ; Mike Peden ; Paul Richetta ; Matt Anderson ; Rich Bennington ; Daryl Frogget ; Scott Roby ; Mark Jensen, Industrial plant asset management system: apparatus and method.
Nasr Hatem N. (Edina MN) Sadjadi Firooz A. (St. Anthony MN) Bazakos Michael E. (Bloomington MN) Amehdi Hossien (Edina MN), Knowledge and model based adaptive signal processor.
Killpatrick Joseph E. (Minneapolis MN) Berndt Dale F. (Plymouth MN) Fritze Keith R. (Long Lake MN) Cary Gregory E. (Mounds View MN), Laser gyro dither strippr gain correction method and apparatus.
Burns Harry A. ; Larson Brent H. ; Brown Larry K., Local device and process diagnostics in a process control network having distributed control functions.
Kram Richard ; Liss Jonathan ; Theophall Peter, Method and apparatus for automatically identifying system faults in an optical communications system from repeater loop gain signatures.
Qin S. Joe (Austin TX) Dunia Ricardo H. (Austin TX) Hayes Randall L. (Georgetown TX), Method and apparatus for detecting and identifying faulty sensors in a process.
Keeler James D. ; Hartman Eric J. ; O'Hara Steven A. ; Kempf Jill L. ; Godbole Devendra B., Method and apparatus for preprocessing input data to a neural network.
Bonoyer John J. ; Flanagan Todd J., Method and apparatus for self-calibration of a coordinated control system for an electric power generating station.
Piche Stephen ; Keeler James David ; Hartman Eric ; Johnson William D. ; Gerules Mark ; Liano Kadir, Method for steady-state identification based upon identified dynamics.
Malloy ; deceased John R. (late of Drexel Hill PA by Virginia M. Malloy ; executrix) Olsen Arthur M. (Pennsburg PA), Method of digital process variable transmitter calibration and a process variable transmitter system utilizing the same.
Shabtai Joseph S. (Salt Lake City UT) Oblad Alex G. (Salt Lake City UT) Tsai Chi H. (Salt Lake City UT), Method of making jet fuel compositions via a dehydrocondensation reaction process.
Lu Zhuxin J. (Glendale AZ) MacArthur J. Ward (Scottsdale AZ) Horn Brian C. (Phoenix AZ), Method of multivariable predictive control utilizing range control.
Lu Zhuxin J. (19513 N. 73rd La. Glendale AZ 85308), Method of optimal scaling of variables in a multivariable predictive controller utilizing range control.
La Chance Ralph Edward ; Sardell Richard ; Landry Donald Francis ; Waibel Helmut, Methods and systems for providing electronic documentation to users of industrial process control systems.
Degeneff Robert C. (Niskayuna NY) Gutierrez Moises R. (Troy NY), Methods for generating models of non-linear systems and components and for evaluating parameters in relation to such non.
Palusamy Sam S. (Murrysville PA) Bauman Douglas A. (Apollo PA) Kozlosky Thomas A. (Oakmont PA) Bond Charles B. (Export PA) Cranford ; III Elwyn L. (Greensburg PA) Batt Theodore J. (Penn Hills PA), Plant maintenance with predictive diagnostics.
Britt Herbert I. (Cambridge MA) Joshi Amol P. (Marlboro MA) Mahalec Vladimir (Sudbury MA) Piela Peter C. (Brighton MA) Venkataraman Swaminathan (Waltham MA), Plant simulation and optimization software apparatus and method using dual execution models.
Nixon Mark ; Havekost Robert B. ; Jundt Larry O. ; Stevenson Dennis ; Ott Michael G. ; Webb Arthur,GBX ; Lucas Mike,GBX, Process control system using standard protocol control-of standard devices and non-standard devices.
Hallee Donald O. (North Easton MA) Lake Harold (Sharon MA) Johansson Kenneth L. (North Grafton MA) Graves Thomas B. (Norton MA), Process control system with improved fault isolation.
Borchers Hans-Werner,DEX ; Otte Ralf,DEX ; Speh Rainer,DEX ; Weisang Claus,DEX, Process diagnosis system and method for the diagnosis of processes and states in an technical process.
MacArthur J. Ward (Minneapolis MN) Wahlstedt David A. (Minneapolis MN) Woessner Michael A. (Minneapolis MN) Foslien Wendy K. (Minneapolis MN), Receding horizon based adaptive control having means for minimizing operating costs.
Wang Qingsu ; Barnett Gerald ; Greig R. Michael ; Cheng Yi, System and method for performing real time data acquisition, process modeling and fault detection of wafer fabrication p.
Steinman Jethro F. ; Kanji M. Gulam ; Chehadeh Yahia C. ; Himmer Richard P. ; Rosa-Bian John J., System and methods for achieving heterogeneous data flow between algorithm blocks in a distributed control system.
Kessel William C. (Watertown MA) Laclaire John L. (Cambridge MA) Lui Andrew L. (West Newton MA) Locke Michael H. (Lakeville MA) Britt Herbert I. (Cambridge MA), System for displaying different subsets of screen views, entering different amount of information, and determining corre.
Bland Dennis L. (Phoenix AZ) Kast John R. (Phoenix AZ), System for making data available to an outside software package by utilizing a data file which contains source and desti.
Papadopoulos A. Dean ; Tanzman Allan ; Baker ; Jr. Richard A. ; Belliardi Rodolfo G. ; Dube Dennis J. W., System for remotely accessing an industrial control system over a commercial communications network.
Boehling Warren A. ; Peck ; III Stephen Albert ; Wheeler Alan Reid, System for replacing control processor by operating processor in partially disabled mode for tracking control outputs.
Bland Dennis L. ; Koontz Sean C. ; Fox Gary L. ; Strilich James A., Systems and methods for providing dynamic data referencing in a generic data exchange environment.
Wan, Jie; Xu, Xiaotian; Cao, Shuyu; Tan, Jern Khang; Guo, Guoxiao; Xi, Wei, Disk drive adjusting rotation speed of disk to compensate for blind area when measuring frequency response of servo control system.
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