Engine Fault Diagnosis Using Sound Source Analysis Based on Hidden Markov Mode 


Vol. 39,  No. 5, pp. 244-250, May  2014


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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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  Cite this article

[IEEE Style]

L. T. Su and J. Lee, "Engine Fault Diagnosis Using Sound Source Analysis Based on Hidden Markov Mode," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 5, pp. 244-250, 2014. DOI: .

[ACM Style]

Le Tran Su and Jong-Soo Lee. 2014. Engine Fault Diagnosis Using Sound Source Analysis Based on Hidden Markov Mode. The Journal of Korean Institute of Communications and Information Sciences, 39, 5, (2014), 244-250. DOI: .

[KICS Style]

Le Tran Su and Jong-Soo Lee, "Engine Fault Diagnosis Using Sound Source Analysis Based on Hidden Markov Mode," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 5, pp. 244-250, 5. 2014.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880