An adaptive multiscale fully convolutional network for bearing fault diagnosis under noisy environments

Intelligent algorithms based on convolutional neural network (CNN) has demonstrated remarkable potential in diagnosing bearing faults. However, Accurate and robust fault diagnosis using CNNs in noisy environments remains a challenge. In this paper, we propose an end-to-end adaptive multiscale fully convolutional network (AMFCN) for intelligent bearing fault diagnosis under various noise environments. Firstly, we enhance noise adaptability by retrofitting raw signals with random sampling. Then the convolution with huge kernel is innovatively applied for wide-area temporal feature extraction and high-frequency noise suppression. By stacked residual adaptive multiscale convolution (Res_AM) blocks, the AMFCN can adaptively adjust the feature weights from different convolution scales and selectively focus on important features. The AMFCN outstrips five advanced baseline models across two bearing fault datasets with various noise environments. The proposed AMFCN significantly enhances the feature extraction ability, noise immunity, and robustness, surpassing conventional CNNs and other advanced multi-scale CNNs.