An expert system for fault diagnosis in internal combustion engines using probability neural network

Jian Da Wu, Peng Hsin Chiang, Yo Wei Chang, Yao jung Shiao

Research output: Contribution to journalArticle

49 Citations (Scopus)

Abstract

An expert system for fault diagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a high-resolution adaptive filter algorithm. The vital features of signals with various fault conditions are obtained and displayed clearly by order figures. Then the sound energy diagram is utilized to normalize the features and reduce computation quantity. In the second stage, the artificial neural network is used to train the signal features and engine fault conditions. In order to verify the effect of the proposed probability neural network (PNN) in fault diagnosis, two conventional neural networks that included the back-propagation (BP) network and radial-basic function (RBF) network are compared with the proposed PNN network. The experimental results indicated that the proposed PNN network achieved the best performance in the present fault diagnosis system.

Original languageEnglish
Pages (from-to)2704-2713
Number of pages10
JournalExpert Systems with Applications
Volume34
Issue number4
DOIs
Publication statusPublished - 2008 May 1

All Science Journal Classification (ASJC) codes

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

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