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CNN-based Automatic Machine Fault Diagnosis Method Using Spectrogram Images  

Kang, Kyung-Won (Dept. of Information Communication & Software Engineering, Tongmyong University)
Lee, Kyeong-Min (College of General Education, Tongmyong University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.21, no.3, 2020 , pp. 121-126 More about this Journal
Abstract
Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. 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
CNN(Convolution Neural Network); Spectrogram; STFT(Short Time Fourier Transform); Machine fault diagnosis; Deep learning; Image Classification;
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