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Fault Diagnosis Method for Automatic Machine Using Artificial Neutral Network Based on DWT Power Spectral Density  

Kang, Kyung-Won (Dept. of Information and Communication Engineering, Tongmyong University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.20, no.2, 2019 , pp. 78-83 More about this Journal
Abstract
Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically abnormal sound on machines using the acoustic emission by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose here an automatic fault diagnosis method of hand drills using discrete wavelet transform(DWT) and pattern recognition techniques such as artificial neural networks(ANN). We first conduct a filtering analysis based on DWT. The power spectral density(PSD) is performed on the wavelet subband except for the highest and lowest low frequency subband. The PSD of the wavelet coefficients are extracted as our features for classifier based on ANN the pattern recognition part. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.
Keywords
Artificial Neural Network; Discrete Wavelet Transform; Machine Fault Diagnosis; Machine Learning; Pattern Recognition;
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