Rolling Bearing Fault Diagnosis Method Based on RSBU-MSCNN under Strong Background Noise
TL;DR
Experiments on multiple datasets show high accuracy under strong noise, confirming the robustness and applicability of the proposed RSBU-MSCNN-based approach for industrial maintenance.
Abstract
Rolling bearings are key elements in rotating machinery, and reliable fault diagnosis is crucial for condition monitoring and maintenance decisions. Under strong background noise, vibration signals are easily distorted, which degrades conventional CNN-based diagnosis. To address this issue, an RSBU-MSCNN-based approach is proposed. First, Gaussian white noise with different signal-to-noise ratios is added to original signals to simulate industrial noise, and one-dimensional vibration signals are transformed into two-dimensional time–frequency representations using CWT. Then, a residual shrinkage module with a soft-threshold function is introduced for adaptive denoising and redundant noise suppression, while multi-channel, multi-scale convolutions enhance robust feature extraction across different receptive fields. Finally, faults are classified using fully connected layers. Experiments on multiple datasets show high accuracy under strong noise, confirming the robustness and applicability of the proposed method for industrial maintenance.