Analysis of techniques for automatic detection and quantification of stiction in control loops

1
Analysis of techniques for automatic
detectionand quantification of stiction in
control loops

• Henrik Manum
• student, NTNU
• (spring 2006 CPC-Lab (Pisa))

Made 23. of July, 2006
2
Agenda

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

3
About Trondheim
4
About Trondheim
5
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.

6
Agenda

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

7
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) gt 0 3.) Integral action in
the controller changes its output 4.) Valve slips
and subject to dynamic friction.
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How to detect stiction

• Popular methods
• Horchs cross-correlation technique

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How to detect stiction

• Popular methods
• Horchs cross-correlation technique

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How to detect stiction

• Popular methods
• Higher-Order Statistics

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How to detect stiction

• Popular methods
• Curve-fitting / Relay Technique

Stiction
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Agenda

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

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How to detect stiction

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

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How to detect stiction

• Pattern recognition techniques
• Neural networks

Neural network
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How to detect stiction

• Pattern recognition techniques
• Simpler Use differentials (Yamashita method)

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Yamashita method
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Yamashita method
(I,I,I,D,D,S,D,I,....,D)
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Yamashita method
sticky movements

• Combined plots

Threshold 2/8 0.25
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Yamashita method

• Matched index

Threshold 2/8 0.25
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Yamashita method

• Implementation

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Yamashita method

• Application to simulated data
• Choudhury model used

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Yamashita method

• Application to simulated data
• Noise-free VERY GOOD

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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?

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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/?)
  for well-tuned cascades. (? is effective delay
  for inner loop)

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Yamashita method

• Set-point changes

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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

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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

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Yamashita method

• Application to plant data
• Observation window
• OK to
• reduce
• obs. window
• to for example
• 720 samples

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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

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Agenda

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

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Patterns and explanations

• Some other patterns were found. For example
• Possible to find physical explanation?

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Patterns and explanations

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

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Patterns and explanations

• Closer look at control equation (PI)

jump from below.
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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.

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Patterns and explanations

• Measurements out of phase
• 4 time-units 40 seconds. Unlikely in this case!

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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 gt 1)
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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.

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Agenda

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

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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
• dont report
• amount of stiction

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Quantification

• Basis Bi-coherence method.

FFT-filtering by setting all unwanted
coefficients to zero and then take the
inverse transform to get filtered data
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Quantification

• Filtering using FFT
• often problematic

Here lower limit too high
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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

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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)

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Quantification

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

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Quantification

• General conic
• Easier set c -1 and solve by least squares
  directly

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Quantification

• Results (ellipsis fitting)
• Very often the optimization problem found
  strange solutions (often imaginary axes)

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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

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Quantification

• Conclusions
• The work did not give industrial-ready results
• I got more insight into time-domain -gt frequency
  domain filtering (FFT-filtering)

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Agenda

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

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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.

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References

• See thesis for complete bibliography
• Thesis should be available from Sigurd Skogestads
  homepage, www.nt.ntnu.no/users/skoge
• Diploma students -gt 2006 -gt manum
• Contains more details about everything and also
  description about software developed.