Fault Diagnosis of Motor Bearings Based on a Convolutional Long Short-Term Memory Network of Bayesian Optimization

As the main driving equipment of modern industrial production activities, if a motor fails, it causes serious consequences. Bearings are the component with the highest motor failure frequency. It is of practical engineering significance to establish a high-precision algorithm diagnostic model for motor bearings. At present, in data-driven motor bearing fault diagnosis methods, the method of manually adjusting hyperparameters is usually adopted in complex network structure models with many hyperparameters. To realize the automatic optimization selection of hyperparameters, in this paper, a motor bearing fault diagnosis algorithm based on a convolutional long short-term memory network of Bayesian optimization (BO-CLSTM) is proposed. The algorithm combines the Bayesian optimization algorithm (BO), a long short-term memory network (LSTM) and the convolutional layer of a convolutional neural network (CNN). It saves the considerable workload of manually adjusting the hyperparameters, has good noise resistance, and realizes the true end-to-end motor bearing fault diagnosis. The proposed method is trained based on the original vibration signal of the bearing, and the accuracy of the final model reaches 100%. In addition, compared with other advanced fault diagnosis methods based on deep learning, the performance of the proposed method is significantly improved.