文献类型：期刊文献

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

作者：Yao, Yong Gui, Gui Yang, Suixian Zhang, Sen

第一作者：Yao, Yong

通信作者：Yao, Y[1]

机构：[1]Sichuan Univ, Sch Mech Engn, Chengdu 610065, Peoples R China;[2]Natl Inst Measurement & Testing, Acoust Lab, Chengdu 610021, Peoples R China;[3]Guizhou Inst Technol, Sch Big Data, Guiyang 550003, Peoples R China

第一机构：Sichuan Univ, Sch Mech Engn, Chengdu 610065, Peoples R China

通信机构：corresponding author), Sichuan Univ, Sch Mech Engn, Chengdu 610065, Peoples R China.

年份：2021

卷号：70

外文期刊名：IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

收录：;EI(收录号：20213710897355);Scopus(收录号：2-s2.0-85114788917);WOS:【SCI-EXPANDED(收录号:WOS:000698641700005)】；

基金：This work was supported by the National Natural Science Foundation of China under Grant 51275325. The Associate Editor coordinating the review process was Lorenzo Ciani.

语种：英文

外文关键词：Acoustic-based diagnosis (ABD); gear fault diagnosis; multistage attention mechanism; recursive denoising learning (RDL)

摘要：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.