A novel feature adaptive extraction method based on deep learning for bearing fault diagnosis

Abstract

Bearing health condition directly affects the reliability of mechanical equipment. Although deep learning (DL) algorithms have achieved great results in the field of bearing fault diagnosis, traditional activation function uses a fixed mathematical formula to achieve non-linear feature transformation, which tend to compress part of the effective fault information and reduce the performance of fault diagnosis. To address this problem, this paper proposes a slope and threshold adaptive activation function with tanh function (STAC-tanh). Establish the relationship between non-linear feature transformation and input signal by automatically adjusting the shape of activation function. Finally, the model can retain valid fault information to improve fault diagnosis performance. Then, combining STAC-tanh and Residual Networks, this paper proposes ResNet-STAC-tanh for bearing fault diagnosis. Experimenting on the two bearing datasets with added noise, the average accuracy of the network reached 90.00% and 90.77%, respectively. The effectiveness of the new method was verified through comparative experiments.