CNN-LSTM method with batch normalization for rolling bearing fault diagnosis

The real-time monitoring of rotating machinery health is very important, and the rolling bearing fault is the focus of research. It is difficult to diagnose the machinery with complex structure efficiently and accurately by traditional fault diagnosis method. Deep learning develops rapidly in the field of mechanical fault diagnosis due to its strong ability of data analysis and learning. To improve the diagnosis accuracy of traditional Convolutional Neural Network (CNN), a CNN-LSTM model with Batch Normalization (BN) was proposed by considering the defects of Long Short Term Memory Network (LSTM) in diagnosis. Through testing with the CWRU bearing dataset, the results demonstrated that the diagnosis accuracy and efficiency of the hybrid model by batch normalization improved. The proposed method obtained a diagnosis result superior to the traditional deep learning fault diagnosis methods and could efficiently and accurately diagnose faults at various positions and degrees under various loads. © 2022 CIMS. All rights reserved.