MRNet: rolling bearing fault diagnosis in noisy environment based on multi-scale residual convolutional network

Vibration signal collection of rolling bearings in the complex working environment often suffers from significant noise interference, rendering traditional fault diagnosis methods ineffective. To address this challenge, we propose a multi-scale residual convolutional network (MRNet) for diagnosing rolling bearing faults in noisy environments. The MRNet model features multiple convolution branches, each of which utilizes kernels with different sizes to capture fault information at different scales, so this multi-scale framework excels at extracting both local and global information from raw fault vibration signals, enhancing fault recognition accuracy. Additionally, we introduce residual blocks to maintain global information during the convolution operations, preventing useful feature information loss. To further improve global feature extraction capability of the network model, a lightweight Transformer module is developed and incorporated, compensating for some global information that the network's front-end might fail to capture. The effectiveness of MRNet is validated by using two publicly available rolling bearing fault datasets and our own experiment dataset. The verification results indicate that MRNet outperforms other comparative models, particularly for complex fault diagnosis in noisy environments.