Wavelet Transform과 CNN을 이용한 회전기기의 고장진단

초록

This paper proposes fault diagnosis and abnormal condition monitoring method by analyzing vibration signals generated from bearing part of rotating equipment based on Wavelet transform (WT) and Convolutional neural network (CNN). The WT is applied to the measured vibration signal and divides it into different scales. The frequency spectrum of the different signals extracts features through the CNN model. In addition, the classifier is eventually used as an error classification method. The integrated structure allows to classify the vibration signal into different working conditions. Since this procedure can be proposed as a method for analyzing a vibration signal measured in real time to detect an abnormal state.