An Adaptive Anti-Noise Neural Network for Bearing Fault Diagnosis Under Noise and Varying Load Conditions

Abstract

Fault diagnosis in rolling bearings is an indispensable part of maintaining the normal operation of modern machinery, especially under the varying operating conditions. In this paper, an end-to-end adaptive anti-noise neural network framework (AAnNet) is proposed to solve the bearing fault diagnosis problem under heavy noise and varying load conditions, which takes the raw signal as input without requiring manual feature selection or denoising procedures. The proposed AAnNet employs the random sampling strategy and enhanced convolutional neural networks with the exponential linear unit as the activation function to increase the adaptability of the neural network. Moreover, the gated recurrent neural networks with attention mechanism improvement are further adopted to learn and classify the features processed by the convolutional neural networks part. Besides, we try to explain how the network works by visualizing the intrinsic features of the proposed framework. And we explore the effect of the attention mechanism in the proposed framework. Experiments show that the proposed framework achieves state-of-the-art results on two datasets under varying operating conditions.

Keywords

• Bearing fault diagnosis
• convolutional neural network
• deep learning
• load domain adaptation
• noisy conditions
• recurrent neural network