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

Intelligent diagnostic algorithms based on convolutional neural networks (CNNs) have shown great potential in diagnosing various conditions. However, accurately and robustly diagnosing faults in noisy situations remains challenging. This study presents an adaptive fully convolutional network (AFCN) for identifying bearing defects in noisy environments. First, we use a novel large kernel convolution method for high-frequency noise reduction and wide-area temporal feature extraction. By utilizing a sequence of stacked residual adaptive convolution blocks, the AFCN achieves a selective emphasis on significant features and adaptive adjustment of feature weights at various convolution scales. The experimental results have shown that the AFCN achieves a diagnostic accuracy of over 90% for the faults in the CWRU dataset under the -8 dB noise and over 77% for the PU dataset in the case of -6 dB noise. The comparison results with five advanced baseline models have demonstrated the superiority of the AFCN in feature extraction, noise immunity, and robustness to the noise environment. The AFCN provides a better adaption to noise interference than conventional CNNs and other advanced adaptive networks.