Analysis of techniques for automatic detection and quantification of stiction in control loops Henrik Manum student, NTNU (spring 2006: CPC-Lab (Pisa)) Made: 23. of July, 2006

Agenda About Trondheim and myself Introduction to stiction and its detection Yamashita stiction detection method Patterns found in sticky valves Quantification of stiction Conclusions

About Trondheim

About myself Professional experience –Summer 2004: Norsk Hydro. Development of flow-sheet solver for the fertilizer industry (Yara). (YASIM) (Group with 1 professor, 1 PhD- engineer, 2 PhD students, and myself.) –Summer 2005: Statoil. Development of company-wide PID tuning rules, and tuning of new LNG plant at Melkøya. Projects, NTNU: –Phase equilibria for sorption enhanced hydrogen production (fall 2004, supervisor prof. De Chen) –Extension of the SIMC rules to oscillatory and unstable processes. (fall 2005) Thesis: –This presentation (spring 2006, University of Pisa) From September 2006: –PhD student with prof. Skogestad on the Norwegian Research Council - funded project “Near-optimal operation of chemical plants using feedback”.

Agenda About Trondheim and myself Introduction to stiction and detection Yamashita stiction detection method Patterns found in sticky valves Quantification of stiction Conclusions

Introduction to stiction MV(OP) plot. In this work we focus on flow loops with incompressible fluids 1.) Valve at rest and subject to static friction 2.) |e(t)| > 0 3.) Integral action in the controller changes its output 4.) Valve slips and subject to dynamic friction.

How to detect stiction Popular methods –Horch’s cross-correlation technique

How to detect stiction Popular methods –Horch’s cross-correlation technique

How to detect stiction Popular methods –Higher-Order Statistics

How to detect stiction Popular methods –Curve-fitting / Relay Technique Stiction

Agenda About Trondheim and myself Introduction to stiction and its detection Yamashita stiction detection method Patterns found in sticky valves Quantification of stiction Conclusions

How to detect stiction Pattern recognition techniques Possible to detect the typical movements using symbolic represenations?

How to detect stiction Pattern recognition techniques –Neural networks Neural network

How to detect stiction Pattern recognition techniques –Simpler: Use differentials (Yamashita method)

Yamashita method

(I,I,I,D,D,S,D,I,....,D)

Yamashita method Combined plots Threshold: 2/8 = 0.25 sticky movements

Yamashita method Matched index Threshold: 2/8 = 0.25

Yamashita method Implementation

Yamashita method Application to simulated data –Choudhury model used

Yamashita method Application to simulated data –Noise-free: VERY GOOD

Yamashita method Application to simulated data –With noise: Performance degraded Important parameters: Sampling time, frequency content of noise (method sensitive to high-frequency noise) Setting sampling time equal to dominant time constant seems good. For case of no stiction, rho_1 high, but rho_3 always below threshold (0.25) –For the case of sampling time equal to dominant time constant and some filtering of the noise, the method seems to work sufficiently good. Good enough for plant data?

Yamashita method Set-point changes: Good as long as set-point changes occur well within band-width for outer loop (assuming linear changes from cascaded loops) –Found with simulation on noise-free data with setpoint changes (See next slide) The band-width for the outer loop is (1/10)*(1/θ) for well-tuned cascades. (θ is effective delay for inner loop)

Yamashita method Set-point changes

Yamashita method Application to plant data –167 industrial flow loops studied –24 of 55 loops same report Yam and PCU PCU: Tool with the 3 methods mentioned earlier implemented (cross-correlation, bi-coherence and relay). –8 more loops reported by Yam 7 of 8 loops sticky by bi-coherence method Last loop was sticky other weeks –Conclusion Works good Reports stiction in about 50% of the cases

Yamashita method Application to plant data –Alteration of sampling time –Seems like increasing the sampling-time is not too dangerous. should be OK. The original was 10 seconds

Yamashita method Application to plant data –Observation window OK to reduce obs. window to for example 720 samples

Yamashita method Application to plant data –Conclusions Detects stiction in about 50% of the cases for which the advanced package reports stiction Identifies the loops with clear stiction patterns Noise level less than worst case in simulations 720 samples

Agenda About Trondheim and myself Introduction to stiction Yamashita stiction detection method Patterns found in sticky valves Quantification of stiction Conclusions

Patterns and explanations Some other patterns were found. For example: Possible to find physical explanation?

Patterns and explanations Reverse action (= negative valve gain) ? In this case no, because of wrong direction in the plot

Patterns and explanations Closer look at control equation (PI) “jump” from below.

Patterns and explanations The valve can (theoretically) also jump “to the left”! This can be a possible explanation for the pattern showed in the example.

Patterns and explanations Measurements out of phase 4 time-units = 40 seconds. Unlikely in this case!

Patterns and explanations Another (and maybe most likely) for why the Yam method failed for the example Strong increase followed by weaker in OP (want: |differential| > 1)

Patterns and explanations Conclusions –More insight into control action on sticky valves achieved. The Yamashita method can easily be extended to cover to cover other known patterns. The “theoretical considerations” in this chapter needs to be checked with real valves.

Agenda About Trondheim and myself Introduction to stiction Yamashita stiction detection method Patterns found in sticky valves Quantification of stiction Conclusions

Quantification Some work already done at the lab with a method developed and implemented in the PCU. –As with stiction detection methods, it could be nice with more methods. –Necessary, as the detection methods don’t report amount of stiction

Quantification Basis: Bi-coherence method. FFT-filtering by setting all unwanted coefficients to zero and then take the inverse transform to get filtered data

Quantification Filtering using FFT –often problematic Here: lower limit too high

Quantification Filtering using FFT –Conclusion Need steady data (best with little SP-changes) Few examples of suitable data in our plant data Using default filter limits did not work good Still needs tuning –Before industrial implementation quite a lot of work needs to be conducted

Quantification Chose to move on to ellipsis fitting... –3 different methods Simple centered and unrotated ellipse General conic with two different constraint specifications (more details in the next slides)

Quantification Simple unrotated ellipse –equation for ellipse in the –Set of observations- least squares

Quantification General conic Easier: set c = -1 and solve by least squares directly

Quantification Results (ellipsis fitting) –Very often the optimization problem found “strange solutions” (often imaginary axes)

Quantification Discussion (ellipsis) –Does guarantee an ellipsis? (Probably not) (See report for derivation) –Setting seems more promising –Obviously still work to do here! Answer questions given above Consider other techniques, such as clustering techniques

Quantification Conclusions –The work did not give “industrial-ready” results –I got more insight into time-domain -> frequency domain filtering (“FFT”-filtering)

Agenda About Trondheim and myself Introduction to stiction Yamashita stiction detection method Patterns found in sticky valves Quantification of stiction Conclusions

Yamashita method proved to work good on industrial data. Findings submitted to ANIPLA 2006 as a conference paper. Hopefully the thesis gives more insight into patterns in sticky valves in MV(OP) plots. Introductory work to filtering and ellipsis fitting for quantification conducted.

References See thesis for complete bibliography Thesis should be available from Sigurd Skogestads homepage, –Diploma students -> > manum –Contains more details about everything and also description about software developed.