The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Engine Fault Diagnosis Using Sound Source Analysis Based on Hidden Markov Model

HMM기반 소음분석에 의한 엔진고장 진단기법

  • Le, Tran Su (School of Electrical Engineering, University of Ulsan) ;
  • Lee, Jong-Soo (School of Electrical Engineering, University of Ulsan)
  • Received : 2014.03.19
  • Accepted : 2014.05.08
  • Published : 2014.05.31
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Abstract

The Most Serious Engine Faults Are Those That Occur Within The Engine. Traditional Engine Fault Diagnosis Is Highly Dependent On The Engineer'S Technical Skills And Has A High Failure Rate. Neural Networks And Support Vector Machine Were Proposed For Use In A Diagnosis Model. In This Paper, Noisy Sound From Faulty Engines Was Represented By The Mel Frequency Cepstrum Coefficients, Zero Crossing Rate, Mean Square And Fundamental Frequency Features, Are Used In The Hidden Markov Model For Diagnosis. Our Experimental Results Indicate That The Proposed Method Performs The Diagnosis With A High Accuracy Rate Of About 98% For All Eight Fault Types.

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References

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