Cellular neural network and its application in the diagnosis of abnormal automobile sound

Zhong Zhang, Hiroaki Kawabata, Eiji Tomita

Research output: Contribution to journalConference article

Abstract

In this paper, a new diagnostic method for abnormal automobile sound using CNN is proposed. The procedure of the method consists of 1) calculating the autoregressive model (AR model) coefficients from the abnormal sound by using the maximum entropy method; 2) constructing the CNN whose memory patterns become standard abnormal sound patterns; 3) making the coefficients obtained as an initial pattern and recalling one from the memory patterns, and then obtaining a diagnosis result. By using the method, the influence of the noise occurring from other normal parts can be avoided and the automobile abnormal sound can be diagnosed. The results obtained demonstrate the advantages of our approach.

Original languageEnglish
JournalSAE Technical Papers
DOIs
Publication statusPublished - Jan 1 2002
EventPowertrain and Fluid Systems Conference and Exhibition - San Diego, CA, United States
Duration: Oct 21 2002Oct 24 2002

ASJC Scopus subject areas

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Pollution
  • Industrial and Manufacturing Engineering

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