1 / 39

Control Performance Monitoring

Control Performance Monitoring. Alf Isaksson, Alexander Horch ABB Corporate Research. PROST Seminar 22 January 200 2. Goal: detect and diagnose malfunctioning control loops. oscillation. or too high variance. Bad control manifests itself as. Methods needed to. detect oscillations

keena
Download Presentation

Control Performance Monitoring

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Control Performance Monitoring Alf Isaksson, Alexander Horch ABB Corporate Research PROST Seminar22 January 2002

  2. Goal: detect and diagnose malfunctioning control loops

  3. oscillation or too high variance Bad control manifests itself as

  4. Methods needed to • detect oscillations • diagnose oscillations • determine of variance is too large Since there are hundreds of loops methods should beautomatic

  5. Oscillation detection • Hägglund (1995). Consider areas between zero crossings (count if large enough). • Stattin and Forsman (1998). Based on same idea, easier to use. • Seborg and Miao (1999). Damping ratio of auto-correlation function.

  6. Stattin index: Compare areas between zero crossings

  7. Oscillation index 0 = no oscillation, 1 = perfect osc. 0.88 0.25 Controller re-tuned

  8. Valve IP converter replaced Oscillation index trend plot index days

  9. 1 0.8 0.6 0.4 0.2 0 0 0.2 0.4 0.6 0.8 1 Major advantage: correlation analysis Conclusion: The loops interact. One of them is likely to cause both oscillations oscillation loop 2 oscillation loop 1

  10. F FC Potential causes are... cycling load static friction tight tuning

  11. process output cross-correlation control signal If the cause is stiction...

  12. cross correlation If the cause is NOT stiction... process output control signal

  13. Stiction diagnosis • New method by Horch (1999) which utilizes that • when stictionin valve, process variable and control signal have odd cross-correlation • when”not stiction” the signals are such that the cross-correlation is even (due to negative feedback)

  14. QC Q F FC Example: two coupled loops Stiction water pulp O.K.

  15. Diagnosis: stiction no stiction Example cont’d concentration loop flow loop data cross-corr.

  16. Important assumptions • O Self-regulating process Oscillation detected Cross-correlation method O.K. Integral action No compressible media

  17. stiction Example II: integrating plant two different level control loops no stiction no stiction

  18. CCF-method useless for integrating plants! Integration destroys the specific correlation in the stiction case. CCF is even, no matter if stiction or not. Re-calculation (differentiation) does not solve the problem level control loop

  19. ... ... ‘Second derivative is infinite’ Idea! Look for discontinuities in the data!

  20. Y dy dt d2y dt2 stiction no stiction 1.) Differentiate the process output!

  21. d2y d2y dt2 dt2 stiction 3a.) Histogram (ideally) no stiction

  22. d2y d2y dt2 dt2 stiction 3b.) Histogram (noise & filter) no stiction

  23. d2y dt2 Level control with stiction y(t) stiction MSE: 0.97 2.01

  24. d2y dt2 Level control without stiction y(t) no stiction MSE: 1.17 0.46

  25. Y dy dt Y’ d2y dt2 Use Camel method also for self-regulatingprocesses! Y stiction no stiction

  26. Detect too large variance (too large 2-sigma) 2σ Basic problem: -2σ Is this good or bad?

  27. Performance index • Introduce a control performance measure: • Possible to calculate denominator from normal operating data given knowledge of process time delay (deadtime). • Proposed by Harris (1989). • Modification presented in Horch and Isaksson (1999) Current variance Ip = Theoretically opt variance

  28. Modified Index: Before: 2.11 1.07 After:

  29. LoopMD ABB 'LATTS' KCL-CoPA LoopAnalyst Commercial tools / suppliers ... PROTUNER™

  30. LATTS – Loop Auditing and Tuning Tool Suite • Process model identification • PID controller tuning • Loop auditing Part of ABB Industrial IT concept and uses the new Aspect Integrator Platform (AIP). Consists of three Aspects:

  31. Process Model Identification Aspect

  32. PID Controller Tuning Aspect

  33. Auditing Aspect • Computes 21 different quantities/indices. For example: • Control error standard deviation • Oscillation index • Stiction diagnosis (correlation) • Stiction diagnosis (histogram) • Modified Harris index

  34. Auditing Aspect cont’d • Combines these indices to test a number of hypotheses, such as • Acceptable performance • Possible valve problem • Sluggish tuning The result is summarized in a report, either as a text file or in Internet Explorer

  35. Auditing -- Index trend plots

  36. Auditing -- Report

  37. Conclusions • Methods exist for non-invasive • Oscillation detection • Stiction diagnosis • Minimum variance benchmark • New ABB Product LATTS under Beta testing right now. Product release approximately June 2002.

  38. Future work (industrial as well as academic) • detection and diagnosis of mill-wide oscillations • distinction of linearly and non-linearly caused oscillations • performance assessment based on full process model (event-triggered estimation) • application of multivariable performance index • performance monitoring of MPC loops

  39. abb

More Related