Multi-scale split dual calibration network with periodic information for interpretable fault diagnosis of rotating machinery

Abstract

Conventional intelligent fault diagnosis algorithms based on signal processing and pattern recognition have high demands on expert experience and poor generalization performance, which may not have good fault diagnosis performance in complex industrial fields. Meanwhile, the data acquisition system may suffer from cyber attacks when collecting vibration signals. The vibration signal has a very low signal-to-noise ratio (SNR), which seriously affects the accuracy of fault diagnosis. Aiming at the problem of fault diagnosis under low SNR, a new fault diagnosis framework based on a Multi-scale Split Dual Calibration Network with Periodic Information (PI-MSDCN) is proposed in this paper. In the fault diagnosis framework, a periodic block is constructed to automatically learn the periodic information of vibration signals through the neural network. The learned periodic information and raw vibration signal are used as the input data of MSDCN. Specifically, MSDCN uses convolution kernels of different sizes for different channels of input features to generate multi-scale features, and obtain mixed domain attention features for features with different scales respectively. Then, the attention feature is used as the threshold to remove the redundant information in the multi-scale feature adaptively. Finally, in order to calibrate the contribution of different scale features to fault diagnosis, the mixed-domain attention coefficients are applied to the corresponding features to obtain richer multi-scale attention features. The experimental studies under different levels of interference are performed to demonstrate the average accuracy of the proposed method is 92.91% (±5.08%), which is superior to other existing results in literature.