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http://dx.doi.org/10.9717/kmms.2019.22.11.1233

Machine Fault Diagnosis Method based on DWT Power Spectral Density using Multi Patten Recognition  

Kang, Kyung-Won (Dept. of Information and Communications Eng., Tongmyong University)
Lee, Kyeong-Min (College of General Education, Tongmyong University)
Vununu, Caleb (Dept. of IT Convergence and Application Eng., Pukyong National University)
Kwon, Ki-Ryong (Dept. of IT Convergence and Application Eng., Pukyong National University)
Publication Information
Journal of Korea Multimedia Society / v.22, no.11, 2019 , pp. 1233-1241 More about this Journal
Abstract
The goal of the sound-based mechanical fault diagnosis technique is to automatically find abnormal signals in the machine using acoustic emission. Conventional methods of using mathematical models have been found to be inaccurate due to the complexity of industrial mechanical systems and the existence of nonlinear factors such as noise. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose an automatic fault diagnosis method using discrete wavelet transform and power spectrum density using multi pattern recognition. First, we perform DWT-based filtering analysis for noise cancelling and effective feature extraction. Next, the power spectral density(PSD) is performed on each subband of the DWT in order to effectively extract feature vectors of sound. Finally, each PSD data is extracted with the features of the classifier using multi pattern recognition. The results show that the proposed method can not only be used effectively to detect faults as well as apply to various automatic diagnosis system based on sound.
Keywords
Atificial Neural Network; Discrete Wavelet Transform; Machine Fault Diagnosis; Machine Learning; Pattern Recognition;
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Times Cited By KSCI : 1  (Citation Analysis)
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