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

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
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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 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 PhDengineer, 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
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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
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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
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
720 samples
• Noise level less than worst case in simulations
Agenda
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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
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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
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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
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About Trondheim and myself
Introduction to stiction
Yamashita stiction detection method
Patterns found in sticky valves
Quantification of stiction
Conclusions
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,
www.nt.ntnu.no/users/skoge
– Diploma students -> 2006 ->
manum
– Contains more details about
everything and also description
about software developed.