A multi-scale dilated convolutional neural network-based bearing fault diagnosis method with dense connections under strong noise conditions

Abstract In recent years, deep learning technology has been widely applied to bearing fault diagnosis, achieving significant progress. However, noise often masks fault characteristics, making feature extraction difficult and reducing diagnostic accuracy, which in turn hinders practical application. Therefore, this paper proposes a fault diagnosis model based on a gating mechanism and multi-scale feature fusion (multi-scale distributed convolution (MSDC)-Dense DGated convolutional neural network (CNN)). This model enhances noise resistance through a two-stage feature learning architecture: The first stage employs an MSDC module, which incorporates three parallel group convolutions with distinct dilation rates. This simultaneously filters noise while capturing multi-scale temporal features. Group CNN reduces parameter redundancy and strengthens local feature extraction. The second stage introduces a Dense DGated CNN block. This block dynamically suppresses noise interference through a product operation between feature branches and dilation-gated branches. A 1–1 convolution layer is added after convolutions with high dilation rates to mitigate the grid effect. Dense connections are employed to strengthen feature correlations. Additionally, the model incorporates a squeeze and excitation module that utilizes channel attention to amplify critical fault features while suppressing noise-dominated redundant information adaptively. The proposed model was validated on the CWRU and self-test datasets under various noise conditions. Experimental results demonstrate that the model achieves a diagnostic accuracy of 89.70% even in high-noise environments with a signal-to-noise ratio of −10 dB. Characterized by minimal parameters and strong anti-interference capabilities, it is well-suited for fault detection tasks in industrial time-series signals that are heavily influenced by substantial noise interference.