A Fault Diagnosis Method of Rotor System Based on Parallel Convolutional Neural Network Architecture with Attention Mechanism

In practical engineering applications, the working load of the rotor system is changing constantly, and the noise pollution of its working environment is serious, which leads to the performance degradation of traditional fault diagnosis methods. To solve the above problems, we present a novel rotor system fault diagnosis model based on parallel convolutional neural network architecture with attention mechanism (AMPCNN). The model uses convolution kernels of different sizes in parallel channels to process raw data, and based on late feature fusion, a more comprehensive feature map is obtained. Furthermore, the information sharing between the two channels is realized through the attention mechanism so that the effective features of one channel can be reflected in another channel. The performance of the model under variable working conditions is verified by the Machinery Fault Database (MAFAULDA), and the average accuracy is 99.58%. By dividing Gaussian white noise from -9 dB to 2 dB into 11 intervals and adding it to the public data of Wuhan University, the noise resistance performance is verified, and the proposed method can obtain 100% diagnosis accuracy even in the high noise condition. The above experiments show that in terms of load adaptability and noise immunity, the method has higher accuracy than traditional deep learning classification methods.