A novel residual global context shrinkage network based fault diagnosis method for rotating machinery under noisy conditions

Author:

Tong JinyuORCID,Tang Shiyu,Zheng JindeORCID,Zhao Hongjie,Wu Yi

Abstract

Abstract In real industrial environments, vibration signals generated during the operation of rotating machinery are typically accompanied by significant noise. Existing deep learning methods often yield unsatisfactory diagnostic results when dealing with noisy signals. To address this problem, a novel residual global context shrinkage network (RGNet) is proposed in this paper. Firstly, to fully utilize the useful information in the raw vibration signal, a multi-sensor fusion strategy based on dispersion entropy is designed as the input of the deep network. Then, the RGNet is designed, which improves the long-distance modeling capability of the deep network while suppressing noise, optimizes the network gradient and computational performance. Finally, the noise suppression ability and feature extraction ability of the RGNet are intuitively revealed through an interpretability study. The advantages of the proposed method are proved through a series of comparison experiments under noisy backgrounds.

Funder

the Open Project of Anhui Province Engineering Laboratory of Intelligent Demolition Equipment, China

the Key Program of Natural Science Research of Higher Education in Anhui Province of China

the National Natural Science Foundation of China

Publisher

IOP Publishing

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3