Diagnosis of Process Nonlinearities and Valve Stiction: Data Driven Approaches (Advances in Industrial Control)

293 Pages·2008·14.85 MB·English

by  Ali Ahammad Shoukat Choudhury

Advances in Industrial Control
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M. A. A. Shoukat Choudhury • Sirish L. Shah •
Nina F. Thornhill
Diagnosis of Process
Nonlinearities and Valve
Stiction
Data Driven Approaches
123
M.A.A.ShoukatChoudhury SirishL.Shah
BangladeshUniversityof UniversityofAlberta
EngineeringandTechnology(BUET) DepartmentChemical&Materials
DepartmentofChemicalEngineering Engineering
Dhaka-1000 EdmontonABT6G2G6
Bangladesh Canada
[email protected]
NinaF.Thornhill
ImperialCollegeLondon
DepartmentofChemicalEngineering
SouthKensingtonCampus
LondonSW72AZ
ISBN:978-3-540-79223-9 e-ISBN:978-3-540-79224-6
LibraryofCongressControlNumber:2008928126
(cid:2)c 2008Springer-VerlagBerlinHeidelberg
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Toallourmentorsandstudents
Series Editors’ Foreword
The series Advances in Industrial Control aims to report and encourage technology
transfer in control engineering. The rapid development of control technology has
an impact on all areas of the control discipline. New theory, new controllers,
actuators, sensors, new industrial processes, computer methods, new applications,
new philosophies…, new challenges. Much of this development work resides in
industrial reports, feasibility study papers and the reports of advanced collaborative
projects. The series offers an opportunity for researchers to present an extended
exposition of such new work in all aspects of industrial control for wider and rapid
dissemination.
Statistical process control was probably the first complete process-monitoring
paradigm. It was conceived to detect when process plant conditions had changed
and process outputs were no longer meeting the desired product specifications. The
underlying science of statistical process control was based on Gaussian statistical
analysis and principles and the method also involves tools designed to uncover or
diagnose the source of the detected process change. These diagnostic tools include
methods such as “cause and effect” analysis and the associated fishbone charts;
however, diagnosing the cause of process change was found to be a much more
difficult problem than simply identifying that process change had occurred.
In the context of closed-loop control system assessment, the seminal work of
Harris and co-workers (1989) initiated new practical methods of determining
whether process control loops are well tuned. A key motivation for this work was
to answer the question often raised by industrial engineers of how to find out
(efficiently and economically) whether any of the thousands of PID control loops
present in a typical industrial plant are poorly tuned and need to be retuned. To
achieve this, the solutions proposed exploited the extensive on-line data that
process computers now store routinely for future analysis. Process control
equipment and software vendors enthusiastically took up the approaches developed
and quite a few controller assessment products are now available commercially.
Academic researchers also found performance assessment to be a fruitful area of
investigation and the Advances in Industrial Control monograph series published
the work of Biao Huang and Sirish Shah as Performance Assessment of Control
Loops (ISBN 978-1-85233-639-4, 1999). More recent contributions to this field
vii
viii Series Editors’ Foreword
were published in the series as the contributed volume, Process Control
Performance Assessment: From Theory to Implementation with Andrzej Ordys,
Damian Uduehi, and Michael Johnson as Editors (ISBN 978-1-84628-623-0,
2007).
Along with this good progress in process controller assessment methods,
researchers have also been investigating techniques to diagnose what is causing the
process or control loop degradation. This requires the use of on-line data to identify
faults via new diagnostic indicators of typical process problems. A significant
focus of some of this research has been the issue of valve problems; a research
direction that has been motivated by some industrial statistics that show up to 40%
of control loops having performance degradation attributable to valve problems.
Shoukat Choudhury, Sirish Shah, and Nina Thornhill have been very active in
this research field for a number of years and have written a coherent and consistent
presentation of their many research results as this monograph, Diagnosis of
Process Nonlinearities and Valve Stiction. The Advances in Industrial Control
series is pleased to welcome this new and substantial contribution to the process
diagnostic literature. The reader will find the exploitation of the extensive process
data archives created by today’s process computer systems one theme in the
monograph. From another viewpoint, the use of higher-order statistics could be
considered to provide a continuing link to the earlier methods of the statistical
process control paradigm. Then there is the significant work presented on control
valves, stiction, and plant-wide disturbances and this demonstrates important new
diagnostic tools that can be used by the industrial process engineer. Finally, the
volume has archival value since it brings together so many new practical results in
one consistent framework. The result is a stimulating volume in the Advances in
Industrial Control series that will be appreciated and used by industrial process
control engineers, and explored further by postgraduate students and control
experts from the academic controls community.
Glasgow M.J. Grimble
Scotland, UK M.A. Johnson
Preface
Classicalcontrolhasmaturedtothepointwhereoff-the-shelfcontrolsolutions,for
linear time-invariant systems, are easily available from most major control ven-
dors. In the relentless drive for improved regulatory performance due to increas-
ing energy costs, higher quality specifications and competition, advanced tools
for monitoring closed-loop performance are becoming available. Such measures
of closed-loop control performance can now be readily computed in an on-line
manner.
Performancemetrics,fromroutineoperatingdata,giveanindicationofclosed-
loopperformancewithrespecttouser-definedbenchmarks,suchasminimumvari-
ancebenchmark,orahistoricalbenchmarkofsatisfactoryperformanceoradesired
settlingtimebenchmark.Conversely,suchbenchmarksalsoprovideanindicationof
thedifficultyofcontrollingaprocess.However,thediagnosisofpoorperformance
isanareawithmanychallenges.Poorclosed-systemperformancemaybeduetoa
varietyofreasons,forexample,poorcontrollertuning,impropercontrollerconfigu-
ration,processoperatingconditionchanges,processconstraints,actuatorproblems
suchasvalvestictionorsaturationandsoon.Thediagnosisofpoorperformanceis
anon-trivialproblem.
Access to routine process data is now a norm. The purpose of this book is to
introducenoveltoolsintheanalysisofsuchdatatodeterminethediagnosisofpoor
controllerperformance.Themainmaterialinthebookisconcernedwiththedetec-
tion, diagnosis and quantification of process nonlinearities. Process changes such
as saturation constraints, sticky valves and nonlinear operating regimes introduce
nonlinearitiesincontrolsignals.Theveryspecificfocusofthisbookisondetecting
processnonlinearitiesanddiagnosingandquantificationofthesourceofsuchnon-
linearities. The main tool for the detection of such nonlinearities are higher-order
statistics, in particular third-order cumulants and the bispectrum, as the spectral
counterpartofthethird-ordermomentsofthedistributionofasignal.Specifically,
thistextprovidesanswerstoquestionssuchasthefollowing:
• Are the process data well-conditioned for analysis in the first place? Well-
conditioned data are an indispensable part of computing any performance met-
ric.Itisfutiletocomputeperformancemetricsandmakediagnosisconclusions
on poorly conditioned data, as the results may lead to erroneous conclusions.
Thisbookdwellsoncheckingtheintegrityofarchiveddatapriortousingsuch
ix
x Preface
data for computing performance metrics. For example, methods for estimating
datacompressionfactorsandsensorquantizationestimatesareconsideredinthis
book.
• Howtodetermineameasureofprocessnonlinearityfromprocessdata?Sucha
metric may indeed give a clue to the degree of difficulty in controlling a given
process.Iftheprocessisdiagnosedtobestronglynonlinearthanthesuggested
remedialactionwouldbetoconsideranonlinearcontrolalgorithm.
• Howtocheckiftheactuatorisperformingsatisfactorily?Statisticsfromseveral
large-scaleindustrialstudieshaveconfirmedthatasmanyas40%ofthecontrol
loopsdonotperformwellbecauseofvalveproblems.Inthisrespect,animpor-
tantpartofthisbookisfocussedondevelopingareliabletestforthedetectionof
valvestiction.Asignificantportionofthismonographisdevotedtowardsdata-
based modelling of valve dynamics and the detection and diagnosis of valve or
actuator problems from routine operating data. Many simulation examples and
over a dozen industrial case studies are provided to illustrate the application of
higher-orderstatisticaltoolsdevelopedinthisbook.
• Are there plant-wide oscillations and if so how to detect loops with common
oscillationsanddiagnosethepossiblesourceoftheoscillations?Itisnotuncom-
mon,asevidentfromthethreeindustrialcasestudiesconsideredhere,formany
loops to have common oscillations. Because of the highly integrated nature of
modern plants, oscillations that may arise in one loop due to overly tuned con-
trollergainsorastickyvalveoranoscillatorydisturbance,caneasilypropagate
tomanyotherloopsdownstreamaswellasupstreamduetothepresenceofre-
cycles.Newlydevelopedtoolsforthedetectionanddiagnosisofsuchproblems
arepresentedtowardstheendofthisbook.
Mostoftheresultspresentedherehaveappearedinthearchivalliterature,albeit
in compact forms, over the last several years. One motivation in preparing this
monograph was to consolidate all these results in a logical and presentable form
in one place. In this respect, the book is likely to be of use for new researchers
asaplacetoaccessbasicinformationpresentedinatutorialformatasspaceisno
longerarestriction,asitisintechnicalpapersinarchivedjournalsandconference
proceedings. However, there are results here that will also appeal to the practising
engineer.Portionsofthisbookcanalsobeusedinagraduate-levelcourseinprocess
control,signalprocessingandfaultdetectionandisolation.Manyofthechaptersdo
notrequireknowledgeofearlierchaptersasprerequisiteinformationandtherefore
canbereadindependently.
Theworkinthisbookhasbeentheoutcomeofseveralyearsofresearcheffortsby
theauthorswithcontributionsfrommanyothergraduatestudentsattheUniversity
ofAlberta,Canada,andintheCentreforProcessSystemsEngineeringatUniversity
College London and Imperial College London, UK, as well as collaborative work
with industry. It is a pleasure to thank the many people who have contributed so
generouslytotheconceptionandcreationofthematerialinthisbook.Theresearch
environment at U of A, UCL and Imperial College has been very stimulating and
superblyconducivetowardsdoingcollaborativeandcross-disciplinaryresearchand
has provided much needed fuel to the efforts that underwent in the work reported