Research on motor bearing fault diagnosis based on BO-PCA-BiLSTM

Motor bearing fault is one of the common problems in motor equipment, and it is very important to accurately identify the type of fault to ensure the smooth operation of the equipment. However, the traditional fault diagnosis methods are often limited by noise interference and difficulty in feature extraction, which leads to low diagnostic accuracy. In order to improve the fault diagnosis rate of motor bearing, a method combining Bayesian optimization, PCA and BiLSTM is proposed to optimize the performance of the neural network model. First, the hyperparameters are optimized by Bayesian optimization, then the dimensionality is reduced by PCA algorithm to extract main features, filter noise and redundant information, and finally the fault features are classified and diagnosed by BiLSTM. Experimental results demonstrate that this method can effectively enhance the accuracy of motor bearing fault diagnosis and finally the accuracy of the test set reaches 99.07%, showing good robustness.