An adaptive anti-noise network with recursive attention mechanism for gear fault diagnosis in real-industrial noise environment condition

Acoustic-based diagnosis (ABD) is a promising method for machinery fault detection due to its ability of noncontact measurement by air-couple. However, most of the ABD methods are constrained by strong and highly non-stationary background noise interference in practical industrial application. To address the shortcoming, a novel anti-noise ABD method based on recursive attention mechanism (RAM) is proposed in this paper. In proposed method, a multi-stage attention module (MSAM) is firstly designed as fundament of RAM to automatically estimate the noise interference probability within time-frequency (T-F) unit of each signal sample. Simultaneously, a recursive learning strategy is introduced to construct RAM by reusing the MSAM for multiple blocks to gradually refine the estimated probability and adaptively simulated noise interference in diagnosis model for enhancing anti-noise diagnosis ability. Then, based on RAM, a domain adaption method is established to endow the model with good cross-domain ability for further improving the anti-noise performance of the diagnosis model. The experiment result in both real-industrial noise condition and stimulated noise conditions with different SNRs indicate that the proposed method has stronger robustness and better generalization ability than other popular methods in dealing with gear fault diagnosis task under noise condition.

Automated summary

This paper proposes a novel machinery fault detection method based on recursive attention mechanism (RAM), which automatically estimates noise interference probability and gradually improves anti-noise diagnosis ability through multiple-stage attention module. Moreover, a domain adaption method is established to enhance the model's cross-domain ability for further improvement in anti-noise performance.