Vibro-acoustic fault detection and diagnosis in hybrid electric vehicle

Author

Salim, G. ; Ouadie, Bennouna ; Ghaleb, Hoblos

Author_Institution

Autom. & Syst. Lab., Inst. de Rech. en Syst. Electroniques Embarques, Rouen, France

fYear

2013

Firstpage

309

Lastpage

313

Abstract

The aim of this paper is to study the effects of faults on vibration and noise spectrum of the Permanent Magnet Synchronous Motor (PMSM) of a hybrid vehicle. In this work, a new approach is used by adding the acoustic noise. This vibro-acoustic modeling allows discovering the influence of faults on spectrum features. Then, a feed forward neural network based on Levenberg-Marquardt training is used for classification. Finally, all the technique is implemented on the PMSM of a hybrid vehicle.

Keywords

acoustic noise; fault diagnosis; feedforward neural nets; hybrid electric vehicles; permanent magnet motors; power engineering computing; synchronous motors; Levenberg-Marquardt training; PMSM; acoustic noise; feed forward neural network; hybrid electric vehicle; hybrid vehicle; permanent magnet synchronous motor; vibro-acoustic fault detection; vibro-acoustic fault diagnosis; Acoustics; Circuit faults; Fault detection; Force; Permanent magnet motors; Stators; Vibrations; Fault detection and diagnosis; PMSM; analytical model; electromagnetic noise; neural network; vibrations;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering, Energy and Electrical Drives (POWERENG), 2013 Fourth International Conference on

Conference_Location

Istanbul

ISSN

2155-5516

Type

conf

DOI

10.1109/PowerEng.2013.6635625

Filename

6635625