An anti-noise fault diagnosis approach for rolling bearings based on multiscale CNN-LSTM and a deep residual learning model

Abstract Bearing fault vibration signals collected in real engineering cases often contain environmental noise which can easily mask the fault type characteristics of vibration signals, making it difficult to determine the corresponding fault type when traditional deep learning methods are used for fault diagnosis. To solve the above problem, a neural network model named multiscale CNN-LSTM (convolutional neural network-long short-term memory) and a deep residual learning model was designed, which combines a multiscale wide CNN-LSTM module and a deep residual module for rolling bearing fault diagnosis. In this model, a wide convolution kernel CNN-LSTM structure with different convolution scales is used to extract a variety of different types of frequency and sequential features from vibration signals. It is worth noting that the wide convolution kernel CNN-LSTM structure not only has stronger feature extraction performance compared with the common convolution layer but can also reduce the interference of high-frequency noise. Moreover, the deep residual module with a wide convolution kernel CNN-LSTM structure is used to further improve the feature expression ability of the proposed model. The above algorithm enables the proposed model to better extract the fault features hidden in the noise signal. When compared with some state-of-the-art methods, the experimental results showed that this model has better anti-noise performance and better generalization ability for rolling bearing fault diagnosis.