Condition Monitoring and Management from Acoustic Emissions

219 Pages·2006·5.69 MB·English

by  Niels Henrik Pontoppidan

Condition Monitoring and
Management from Acoustic
Emissions
Niels Henrik Pontoppidan
Kongens Lyngby 2005
IMM-PHD-2005-147
Technical University of Denmark
Informatics and Mathematical Modelling
Building 321, DK-2800 Kongens Lyngby, Denmark
Phone [phone], Fax [phone]
[email protected]
www.imm.dtu.dk
IMM-PHD: ISSN 0909-3192
Summary
In the following, I will use technical terms without explanation as it gives the
freedom to describe the project in a shorter form for those who already know.
The thesis is about condition monitoring of large diesel engines from acoustic
emission signals. The experiments have been focused on a specific and severe
fault called scuffing. The fault is generally assumed to arise from increased
interaction between the piston and liner. For generating experimental data
destructive tests with no lubrication, oil has been carried out. Focus has been
on modeling the normal condition and detecting the increased interaction due
to the lack of lubrication as a deviation from the normal.
Linear instantaneous blind source separation is capable of picking out the rel-
evant hidden signals. Those hidden signals and the estimated noise level can
be used to model the normal-condition, and faults can be detected as outliers
in that model. Among the investigated methods the Mean field independent
component analysis with diagonal noise covariance matrix models is best at
modelingtheobservedsignals. Nevertheless,thisdoesnotimplythatthisisthe
best model to detect the outliers.
Anothercontributionofthisworkistheanalysisoftheangularpositionchanges
oftheenginerelatedeventssuchasfuelinjectionandvalveopenings, causedby
operational load changes. With inspiration from speech recognition and voice
effects the angular timing changes have been inverted with the event alignment
framework. Withtheeventalignmentframeworkitisshownthatnon-stationary
condition monitoring can be achieved.
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Resum´e
Emnet i denne PhD afhandling er tilstandsoverv˚agning ved brug af ultralyd i
store diesel motorer, der bruges til skibe. M˚alet har været at kunne detektere
en specifik og alvorlig fejl kaldet: Scuffing. Idet menes at fejlen opst˚ar ved
kontaktmellemcylindervægenogstempleterfølgendeeksperimentudført: Ved
afbrydelse af smøreolien til cylinderen er det forsøgt at fremprovokere Scuffing.
Efterfølgendeerdetforsøgt,atladealgoritmertrænetp˚adetnormalelydbillede,
at detektere det ændrede lydbillede som følge af den manglende smøring.
Lineær instantan blind signal separation kan finde de relevante skjulte signaler.
Disseskjultesignalerkanbrugestilatmodellerenormaltilstandensammenmed
det estimerede støj niveau. Fejl kan følgelig detekteres som afvigere fra denne
model. Blandt de undersøgte metoder er Mean field independent components
analysis, med diagonal støj kovarians matrice, den bedste til at modellere de
observerede signaler. Men det vises ogs˚a at det ikke nødvendigvis medfører at
dette er den bedste metode til fejl-detektion.
Vinkelforskydninger i motorens lydbillede, eksempelvis indsprøjtning og ventil
˚abning,for˚arsagetafdeoperationelletilstandsændringererblevetanalyseretog
modelleret med signal behandling inspireret af tale genkendelse og lydeffekter
til musik. Denne metode kaldet event alignment muliggør tilstandsoverv˚agning
med skiftende operationelle tilstande, dvs. under skiftende belastninger.
iv
Preface
Thisthesis was preparedatInformatics MathematicalModelling, theTechnical
University of Denmark in partial fulfillment of the requirements for acquiring
the Ph.D. degree in engineering.
The thesis deals with various aspects of mathematical modeling of the engine
conditionwithacousticemissionsignals. Thetwomaintopicsareapplicationof
generative linear models for condition monitoring and non-stationarity through
eventalignment. Itisbasedonthetopicsandresearchinrelationtotheenclosed
research papers written during the period 2002–2005, and elsewhere published.
The thesis was defended on October 6, 2005 at DTU. The review committee
consisted of: Professor Lars Kai Hansen, DTU (Chairman), Professor Fred-
erik Gustafsson, Link¨oping University, Sweden, and Dr. John Alexander Steel,
Heriot-Watt University, Edinburgh, Scotland. The Ph.D. degree in engineering
was awarded on November 18, 2005.
Lyngby, January 2006
Niels Henrik Pontoppidan
vi
Enclosed research papers
Journal papers
[Appendix A] N.H.PontoppidanandS.Sigurdsson.Independentcomponents
in acoustic emission energy signals from large diesel engines. Submitted
to International Journal of COMADEM, 2005. URL http://www2.imm.
dtu.dk/pubdb/p.php?id=3885
[Appendix B] N. H. Pontoppidan, S. Sigurdsson, and J. Larsen. Condition
monitoringwithmeanfieldindependentcomponentsanalysis. Mechanical
SystemsandSignalProcessing,19(6):1337–1347,nov2005b.URLhttp://
dx.doi.org/10.1016/j.ymssp.2005.07.005.SpecialIssue:BlindSource
Separation
Conference papers
[Appendix C] N.H.Pontoppidan,J.Larsen,andS.Sigurdsson.Non-stationary
condition monitoring of large diesel engines with the AEWATT toolbox.
InPuseyetal.[2005].URLhttp://www.imm.dtu.dk/pubdb/p.php?3351.
In Proceedings of MFPT59.
[Appendix D] RunarUnnthorsson,NielsHenrikPontoppidan,andMagnusThor
Jonsson. Extracting information from conventional AE features for fa-
tigue onset damage detection in carbon fiber composites. In Pusey et al.
[2005]. URL http://www.imm.dtu.dk/pubdb/p.php?3289. In Proceed-
ings of MFPT59.
viii
[Appendix E] N. H. Pontoppidan and J. Larsen. Non-stationary condition
monitoring through event alignment. In IEEE Workshop on Machine
Learning for Signal Processing, pages 499–508, Piscataway, New Jersey,
September 2004. IEEE Press. URL http://isp.imm.dtu.dk/mlsp2004
[Appendix F] NielsHenrikPontoppidanandRyanDouglas. Eventalignment,
warpingbetweenrunningspeeds.InRaoetal.[2004],pages621–628.ISBN
0-954 1307-1-5. URL http://www.imm.dtu.dk/pubdb/p.php?3111. In
Proceedings of COMADEM 2004.
[Appendix G] N. H. Pontoppidan and J. Larsen. Unsupervised condition
changedetectioninlargedieselengines. InC.Molina,T.Adali,J.Larsen,
M. Van Hulle, S. Douglas, and Jean Rouat, editors, 2003 IEEE Work-
shop on Neural Networks for Signal Processing, pages 565–574, Piscat-
away, New Jersey, September 2003. IEEE Press. URL http://isp.imm.
dtu.dk/nnsp2003
[Appendix H] N. H. Pontoppidan, J. Larsen, and T. Fog. Independent com-
ponent analysis for detection of condition changes in large diesels. In
Shrivastav et al. [2003]. ISBN 91-7636-376-7. URL http://www.imm.
dtu.dk/pubdb/p.php?2400. In Proceedings of COMADEM 2003.
Various material (not enclosed)
(cid:3) N.H. Pontoppidan, S. Sigurdsson, and J. Larsen. AEWATTtoolbox for
MATLAB. only available through licensing, 2005c
(cid:3) AEWATT Project Consortium. Deliverable 10, Detection and decision
making methods for automated condition monitoring and management of
machines. Technical report, July 2005
(cid:3) AEWATT Project Consortium. Mid Term Assessment Report. Technical
report, June 2004b
(cid:3) AEWATT Project Consortium. Deliverable 8, Data Acquisition Strategy
and Signal Preprocessing. Technical report, January 2004a
(cid:3) AEWATT Project Consortium. Deliverable 2, AE propagation and sig-
nal/event correlation. Technical report, 2003a