An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network

Jian-Da Wu, Jun Ming Kuo

Research output: Contribution to journalArticle

47 Citations (Scopus)

Abstract

This paper describes a fault diagnosis system for automotive generators using discrete wavelet transform (DWT) and an artificial neural network. Conventional fault indications of automotive generators generally use an indicator to inform the driver when the charging system is malfunction. But this charge indicator tells only if the generator is normal or in a fault condition. In the present study, an automotive generator fault diagnosis system is developed and proposed for fault classification of different fault conditions. The proposed system consists of feature extraction using discrete wavelet analysis to reduce complexity of the feature vectors together with classification using the artificial neural network technique. In the output signal classification, both the back-propagation neural network (BPNN) and generalized regression neural network (GRNN) are used to classify and compare the synthetic fault types in an experimental engine platform. The experimental results indicate that the proposed fault diagnosis is effective and can be used for automotive generators of various engine operating conditions.

Original languageEnglish
Pages (from-to)9776-9783
Number of pages8
JournalExpert Systems with Applications
Volume36
Issue number6
DOIs
Publication statusPublished - 2009 Aug 1

Fingerprint

Discrete wavelet transforms
Failure analysis
Neural networks
Engines
Wavelet analysis
Backpropagation
Feature extraction

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

Cite this

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An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network. / Wu, Jian-Da; Kuo, Jun Ming.

In: Expert Systems with Applications, Vol. 36, No. 6, 01.08.2009, p. 9776-9783.

Research output: Contribution to journalArticle

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