The European Physical Journal Applied Physics
- The European Physical Journal Applied Physics / Volume 43 / Issue 02 / August 2008, pp 245-251
- © EDP Sciences, 2008
- Published online by Cambridge University Press: 31 January 2011
- DOI: http://dx.doi.org/10.1051/epjap:2008118 (About DOI)
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Table of Contents - August 2008 - Volume 43, Issue 02 (Reliability in Electromagnetic Systems (IET))
Reliability in Electromagnetic Systems (IET)
Instrumentation and Metrology
Application of classification methods in fault detection and diagnosis of inverter fed induction machine drive: a trend towards reliability*
C. Delphaa1, D. Dialloa2, M. El Hachemi Benbouzida3 and C. Marchanda2
L2S, CNRS UMR 8506, Supélec, Université Paris Sud 11, Paris, France
LGEP/Spee Labs, CNRS UMR 8507, Supélec, Université Paris Sud 11, Université Pierre et Marie Curie–Paris 6, France
LBMS, IUT de Brest, Electrical Engineering Department, University of Western Brittany, Rue de Kergoat, BP 93169, 29231 Brest Cedex 3, France
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 are transformed 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.
(Received June 27 2007)
(Revised April 1 2008)
(Accepted May 30 2008)
(Online publication July 19 2008)
PACS:
- 02.50.Sk – Multivariate analysis
Footnotes
* This article has been submitted as part of “IET – Colloquium on Reliability in Electromagnetic Systems”, 24 and 25 May 2007, Paris
