A Recursive Denoising Learning for Gear Fault Diagnosis Based on Acoustic Signal in Real Industrial Noise Condition

Abstract

Acoustic-based diagnosis (ABD) is a promising method for machinery fault detection due to its ability to overcome the limitation of vibration measurement through non-contact measurement by air couple. However, most of the ABD approaches are not widely used in real industrial scenario due to the limitation of strong and highly non-stationary background noise interference. To address the shortcoming, a novel ABD method based on recursive denoising learning (RDL) is proposed in this article. In the proposed method, a new multistage attention mechanism is designed as the fundament of RDL for adaptive tracking and estimating non-stationary industrial background noise and automatic suppressing noise. Based on the multistage attention mechanism, a novel recursive learning strategy is introduced to further improve the performance of noise suppression by recursive tracking noise component and gradual denoising in coarse-to-fine manner. Then, an information fusion method, which is based on an improved tiny-shuffle network (TSN), is adopt to increase the discriminative representation of fault feature through fusion of multi-channel denoising information for improving diagnosis accuracy. Afterward, an RDL-based fault diagnosis method is finally obtained by combining with a standard fault diagnosis model, and it eventually achieves good performance for detection gear fault pattern in noise interference environment. The experimental results in both real industrial background noise condition and additive white Gaussian noise (AWGN) condition with different signal-to-noise ratios (SNRs) indicate that the proposed method performs better than all other popular methods in noise suppression and gear fault pattern detection, which verify the effectiveness of the proposed ABD method in dealing with gear fault diagnosis task under noise condition.