关键词: 故障诊断, 滚动轴承, 残差网络, 噪声干扰

Abstract: The ranges of convolutional neural network feature extraction were constrained by the size of the receptive field of the convolutional kernel, and it was difficult for a single-scale convolutional kernel to adequately capture the frequency components of different shocks. A dynamic wide kernels residual network(DWResNet) bearing fault diagnosis method was proposed to solve the above problems which made it difficult to improve the accuracy of bearing fault diagnosis. A wide residual kernel structure to the one-dimensional deep residual network framework was introduced. A parallel two-channel network structure for the feature of bearing faults was constructed. Then, the convolutional kernel was dynamically weighted by the network through an attention mechanism, which was adapted to extract feature information at different scales fully. As a result, effective identification of bearing faults was achieved. Experiments show that the proposed method achieves an accuracy of over 98% for bearing fault diagnosis tasks with different noise levels. Therefore, the dynamic scale weighting mechanism may effectively improve the bearing fault diagnosis, especially in the presence of high noise levels.

Key words: fault diagnosis, rolling bearing, residual network, noise interference