Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-25T18:17:32.537Z Has data issue: false hasContentIssue false

Application of classification methods in fault detection and diagnosis of inverter fed induction machine drive: a trend towards reliability*

Published online by Cambridge University Press:  19 July 2008

C. Delpha*
Affiliation:
L2S, CNRS UMR 8506, Supélec, Université Paris Sud 11, Paris, France
D. Diallo
Affiliation:
LGEP/Spee Labs, CNRS UMR 8507, Supélec, Université Paris Sud 11, Université Pierre et Marie Curie–Paris 6, France
M. El Hachemi Benbouzid
Affiliation:
LBMS, IUT de Brest, Electrical Engineering Department, University of Western Brittany, Rue de Kergoat, BP 93169, 29231 Brest Cedex 3, France
C. Marchand
Affiliation:
LGEP/Spee Labs, CNRS UMR 8507, Supélec, Université Paris Sud 11, Université Pierre et Marie Curie–Paris 6, France
Get access

Abstract

The aim of this paper is to present a method of detection and isolation of intermittent misfiring in power switches of a three phase inverter feeding an induction machine drive. The detection and diagnosis procedure is based solely on the output currents of the inverter flowing into the machine windings. The measured currents aretransformed in the two dimensional frame obtained with the Concordia transform. The data are then treated by a time-average method. The results even promising lack of accuracy mainly in the fault isolation step. To enhance the fault detection and diagnosis by the use of the information enclosed in the data, a Principal Component Analysis classifier is applied. The detection of a fault occurrence is made by a two-class classifier. The isolation is a two-step approach which uses the Linear Discriminant Analysis; the first is to identify the faulty leg with a three-class classifier and the second one discriminates the faulty power switch. Both methods are evaluated with experimental data and pattern recognition method proves its effectiveness and accuracy in the faulty leg detection and isolation.

Keywords

Type
Research Article
Copyright
© EDP Sciences, 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

This article has been submitted as part of “IET –Colloquium on Reliability in Electromagnetic Systems”, 24 and 25 May 2007, Paris

References

Kastha, D. et al., IEEE Trans. Ind. Appl. 30, 426 (1994) CrossRef
Z. Ye et al., Simulation of Electrical faults of three phase induction motor drive system, in CD-ROM Proceedings of IEEE-PESC'01, Vancouver, Canada (IEEE, 2001)
Benbouzid, M.E.H., IEEE Trans. Energy Convers. 14, 1065 (1999) CrossRef
Dolins, S.B. et al., IEEE Trans. Ind. Appl. 28, 261 (1992) CrossRef
Peuget, R. et al., IEEE Trans. Ind. Appl. 34, 1318 (1998) CrossRef
Ribeiro, R.L.D.A. et al., IEEE Trans. Power Electron. 18, 587 (2003) CrossRef
F. Filippetti et al., in Proceedings of ICEM'88, Pisa, Italy, pp. 289–296
Sottile, J. et al., IEEE Trans. Ind. Appl. 30, 1326 (1994) CrossRef
Filippetti, F. et al., IEEE Trans. Ind. Appl. 31, 892 (1995) CrossRef
Filippetti, F. et al., IEEE Trans. Ind. Appl. 34, 98 (1998) CrossRef
Diallo, D. et al., IEEE Trans. Energy Convers. 20, 512 (2005) CrossRef
D.F. Morrisson, Multivariate statistical methods, 2nd edn. (McGraw-Hill, Singapore, 1988), p. 415
M. Jambu, Méthodes de bases de l'analyse de données (Paris, Eyrolles, 1999), p. 412
J. Shawe-Taylor, N. Cristianini, Kernel Methods for Pattern Analysis (Cambridge University Press, 2004)
V. Vapnik, The nature of statistical learning theory (Springer Verlag, 1995), p. 314