Attention Activation Network for Bearing Fault Diagnosis Under Various Noise Environments

Bearings are critical in mechanical systems, as their health impacts system reliability. Proactive monitoring and diagnosing of bearing faults can prevent significant safety issues. Among various diagnostic methods that analyze bearing vibration signals, deep learning is notably effective. However, bearings often operate in noisy environments, especially during failures, which poses a challenge to most current deep learning methods that assume noise-free data. Therefore, this paper designs a Multi-Location Multi-Scale Multi-Level Information Attention Activation Network (MLSCA-CW) with excellent performance in different kinds of strong noise environments by combining soft threshold, self-activation, and self-attention mechanisms. The model has enhanced filtering performance and multi-location information fusion ability. Our comparative and ablation experiments demonstrate that the model's components, including the multi-location and multi-scale vibration extraction module, soft threshold noise filtering module, multi-scale self-activation mechanism, and layer attention mechanism, are highly effective in filtering noise from various locations and extracting multi-dimensional features. The MLSCA-CW model achieves 92.02% accuracy against various strong noise disturbance and outperforms SOTA methods under challenging working conditions in CWRU dataset.