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Engine Fault Diagnosis Using Sound Source Analysis Based on Hidden Markov Model
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 Title & Authors
Engine Fault Diagnosis Using Sound Source Analysis Based on Hidden Markov Model
Le, Tran Su; Lee, Jong-Soo;
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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.
 Keywords
Hmm;Engine Fault;Mfcc;Rms;Bayes;
 Language
English
 Cited by
 References
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