A rolling bearing fault diagnosis method based on a convolutional neural network with frequency attention mechanism

A convolutional neural network fault diagnosis method based on frequency attention mechanism was designed for the problem that the traditional method cannot adaptively extract effective feature information in rolling bearing fault diagnosis and the diagnosis effect of rolling bearing is poor under strong environmental noise interference. Firs, the Mel-frequency cepstral coefficient (MFCC) of the bearing vibration signal was extracted. Second, to solve the problem of the channel attention mechanism adopting global average pooling (GAP) and neglecting channel internal characteristic information, the GAP was extended in the frequency domain, and a two-stage frequency component selection criterion was designed. The results show that the MFCC method can extract fault-sensitive features in industrial noise environments, improve the existing channel attention mechanism using frequency domain attention mechanism, and overcome the information loss caused by GAP of convolutional layer features in channel attention mechanism. Identification accuracy, recall rate, and F1-score are 100% on the rolling bearing simulation fault datasets of Case Western Reserve University and Central South University. Compared with the convolutional block attention module, the accuracy of the method combining spatial attention mechanism and channel attention mechanism is improved by 0.34 and 0.24%, respectively, and compared with other front-bearing fault diagnosis methods, it also offers significant improvement.