Adversarial training of multi-scale channel attention network for enhanced robustness in bearing fault diagnosis

Abstract

For bearing fault diagnosis problems in extremely noisy environments, this paper proposes an innovative universal adversarial training method. This method dynamically introduces noise into the training data, adaptively optimizing the model's robustness. It applies to any neural network without incurring additional computational overhead in the reasoning process. Additionally, we introduce the Multi-Scale Channel Attention Network (MSCAN). This network employs stacked convolutional kernels of varying sizes to extract features at different scales from the input signal. The incorporation of the channel attention mechanism allocates distinct weights to features of different scales, further enhancing the network's representational capacity. Moreover, an AutoML-based automated tuning approach is employed to optimize the model training process, aiding in improving inference accuracy. Compared to existing designs, MSCAN exhibits exceptional accuracy. Through adversarial training, it maintains a 99.44% accuracy rate on the CWRU dataset under strong -3dB noise conditions. On the PU dataset at 0dB, this adversarial training significantly improves the testing accuracy of various models by an average of 36.42%.