A noise-robust CNN architecture with global attention and gated convolutional Kernels for bearing fault detection

Abstract

Detecting faults in bearings is essential for the maintenance and operation of rotating machinery. However, achieving high accuracy and noise immunity is challenging due to the involvement of intricate and noisy signals. To address this issue, this paper introduces a multi-scale separable gated convolutional neural network (GCK-MSSC). In the GCK-MSSC model, the gate convolutional kernel replaces the conventional convolutional kernel. It is designed to dynamically adjust the convolution kernel's weights based on the input features. Additionally, the one-dimensional global attention mechanism (1D-GAM) is incorporated, enhancing the model's global awareness within the multi-scale separable neural network (MSSC) framework. The experimental results on two public bearing datasets confirm the performance of the proposed method. It demonstrates improved performance over current leading-edge methods, especially in terms of accuracy, and proves to be significantly robust against various levels of noise. Specifically, achieves accuracies of 99.45 \% and 99.78 \% on the two datasets. Furthermore, even after the addition of noise with a signal-to-noise ratio of 0, it still maintains an accuracy as high as 85.65\% (on the Politecnico di Torino dataset).