Noise-robust machinery fault diagnosis based on self-attention mechanism in wavelet domain

Abstract

Machinery fault diagnosis in noisy scenes is important and challenging to ensure the safety and stability of machinery. Existing machinery fault diagnosis methods mostly handle noisy signals in the time or frequency domains. However, these methods lack fully consideration of the noise characteristics in both domains, resulting in unstable performance. This paper proposes an anti-noise Wavelet based Self-attention Network for machinery fault diagnosis, named Wavelet-SANet. Our approach combines the frequency-oriented fusion modules and the Transformer modules to restrain noise in both frequency and time domains. Firstly, the multiple frequency-oriented fusion modules exploit a self-attention mechanism to weight different frequency features of noisy signals in the wavelet domain for filtering the persistent noise. Furthermore, the Transformer modules are designed to suppress the remaining scattered impulse noise in the time domain. The experimental results on two public bearing datasets demonstrate the promising performance for machinery fault diagnosis. Experimental results on CWRU and SEU datasets indicate the superior performance of Wavelet-SANet on different SNRs.