Research on Fault Detection of Rolling Bearing Based on CWT-DCCNN-LSTM.

As one of the key components in many fields, rolling bearing fault detection is very important. Rolling bearing is in complex and changeable working conditions, so it is challenging to detect its fault. Because the traditional method has weak adaptability in complex and changeable situations, it needs to rely on the opinions of experts more often. Deep learning methods can make up for the shortcomings of traditional methods. Therefore, this paper proposes a method combining continuous wavelet transform (CWT), dual-channel convolutional neural network(DCCNN), and long short-term memory network (LSTM), mainly for fault detection of vibration signals of rolling bearings. Firstly, the vibration signal is denoised by CWT, then the feature of the vibration signal is extracted by DCCNN, and finally, the time series of the vibration signal is extracted by LSTM. Compared with CNN, CWT-CNN, CNN-LSTM, and CWT-CNN-LSTM four models, and analyzed the parameters of the model. The results show that the accuracy of CWT-DCCNN-LSTM model detection is better than other models, and the accuracy rate reaches 99.98 %. [ABSTRACT FROM AUTHOR]