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Support vector machine based classification in condition monitoring of induction motors

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Pöyhönen, Sanna
dc.date.accessioned 2012-02-13T12:48:12Z
dc.date.available 2012-02-13T12:48:12Z
dc.date.issued 2004-06-18
dc.identifier.isbn 951-22-7155-9
dc.identifier.issn 0356-0872
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/2425
dc.description.abstract Continuous and trouble-free operation of induction motors is an essential part of modern power and production plants. Faults and failures of electrical machinery may cause remarkable economical losses but also highly dangerous situations. In addition to analytical and knowledge-based models, application of data-based models has established a firm position in the induction motor fault diagnostics during the last decade. For example, pattern recognition with Neural Networks (NN) is widely studied. Support Vector Machine (SVM) is a novel machine learning method introduced in early 90's. It is based on the statistical learning theory presented by V.N. Vapnik, and it has been successfully applied to numerous classification and pattern recognition problems such as text categorization, image recognition and bioinformatics. SVM based classifier is built to minimize the structural misclassification risk, whereas conventional classification techniques often apply minimization of the empirical risk. Therefore, SVM is claimed to lead enhanced generalisation properties. Further, application of SVM results in the global solution for a classification problem. Thirdly, SVM based classification is attractive, because its efficiency does not directly depend on the dimension of classified entities. This property is very useful in fault diagnostics, because the number of fault classification features does not have to be drastically limited. However, SVM has not yet been widely studied in the area of fault diagnostics. Specifically, in the condition monitoring of induction motor, it does not seem to have been considered before this research. In this thesis, a SVM based classification scheme is designed for different tasks in induction motor fault diagnostics and for partial discharge analysis of insulation condition monitoring. Several variables are compared as fault indicators, and forces on rotor are found to be important in fault detection instead of motor current that is currently widely studied. The measurement of forces is difficult, but easily measurable vibrations are directly related to the forces. Hence, vibration monitoring is considered in more detail as the medium for the motor fault diagnostics. SVM classifiers are essentially 2-class classifiers. In addition to the induction motor fault diagnostics, the results of this thesis cover various methods for coupling SVMs for carrying out a multi-class classification problem. en
dc.format.extent 57
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Helsinki University of Technology en
dc.publisher Teknillinen korkeakoulu fi
dc.relation.ispartofseries Report / Helsinki University of Technology, Control Engineering Laboratory en
dc.relation.ispartofseries 141 en
dc.relation.haspart Pöyhönen S., Negrea M., Arkkio A., Hyötyniemi H. and Koivo H., 2002. Support vector classification for fault diagnostics of an electrical machine. Proceedings of the 6th International Conference on Signal Processing (ICSP'02). Beijing, China, 26-30 August 2002, volume 2, pages 1719-1722.
dc.relation.haspart Pöyhönen S., Negrea M., Arkkio A., Hyötyniemi H. and Koivo H., 2002. Fault diagnostics of an electrical machine with multiple support vector classifiers. Proceedings of the 17th IEEE International Symposium on Intelligent Control (ISIC'02). Vancouver, British Columbia, Canada, 27-30 October 2002, volume 1, pages 373-378.
dc.relation.haspart Pöyhönen S., Arkkio A. and Hyötyniemi H., 2003. Coupling pairwise support vector machines for fault classification. Proceedings of the 5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (Safeprocess2003). Washington D. C., USA, 9-11 June 2003, pages 705-710.
dc.relation.haspart Pöyhönen S., Negrea M., Jover P., Arkkio A. and Hyötyniemi H., 2003. Numerical magnetic field analysis and signal processing for fault diagnostics of electrical machines. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 22, number 4, pages 969-981. [article4.pdf] © 2003 Emerald. By permission.
dc.relation.haspart Pöyhönen S., Jover P. and Hyötyniemi H., 2004. Signal processing of vibrations for condition monitoring of an induction motor. Proceedings of the 1st IEEE-EURASIP International Symposium on Control, Communications, and Signal Processing (ISCCSP 2004). Hammamet, Tunisia, 21-24 March 2004, pages 499-502.
dc.relation.haspart Pöyhönen S., Jover P. and Hyötyniemi H., 2003. Independent component analysis of vibrations for fault diagnosis of an induction motor. Proceedings of the IASTED International Conference on Circuits, Signals, and Systems (CSS 2003). Cancun, Mexico, 19-21 May 2003, volume 1, pages 203-208. [article6.pdf] © 2003 ACTA Press / International Association of Science and Technology for Development (IASTED). By permission.
dc.relation.haspart Pöyhönen S., Conti M., Cavallini A., Montanari G. C. and Filippetti F., 2004. Insulation defect localization through partial discharge measurements and numerical classification. Proceedings of the 2004 IEEE International Symposium on Industrial Electronics (ISIE 2004). Ajaccio, France, 4-7 May 2004, pages 417-422.
dc.subject.other Mechanical engineering en
dc.subject.other Automation en
dc.title Support vector machine based classification in condition monitoring of induction motors en
dc.type G5 Artikkeliväitöskirja fi
dc.description.version reviewed en
dc.contributor.department Department of Automation and Systems Technology en
dc.contributor.department Automaatio- ja systeemitekniikan osasto fi
dc.subject.keyword fault diagnostics en
dc.subject.keyword induction motor en
dc.subject.keyword support vector machine en
dc.subject.keyword classification en
dc.identifier.urn urn:nbn:fi:tkk-003701
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en
dc.contributor.lab Laboratory of Control Engineering en
dc.contributor.lab Systeemitekniikan laboratorio fi
local.aalto.digifolder Aalto_82931
local.aalto.digiauth ask

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