中国机械工程2019，Vol.30Issue(2)：205-211,7.DOI:10.3969/j.issn.1004-132X.2019.02.011
深度置信网络在齿轮故障诊断中的应用
Gear Fault Diagnosis Based on DBNS
摘要
Abstract
Aiming at the problems of gears and other parts in a gear transmission system that were prone to ault or failure, this paper presented a fault diagnosis method for gear transmissions based on deep learning theory.Firstly, the powerful feature self-extraction ability of DBNs was used to extract the features of the vibration signals of the gear transmission systems.Then the fault signals were identified by the complex map representation capability of DBNs.The diagnosis examples show that if the original time-domain signals of gear vibration are not extracted, the correct recognition rate may only reach about 60％ when directly using DBNs to diagnose.If a simple Fourier transform is applied to the time domain signals, then DBNs may be used to diagnose the frequency spectrum of the processed vibration signals.The accuracy rate may reach 99.7％, which confirms the simplicity and effectiveness of the fault diagnosis method described herein.关键词
齿轮传动/特征提取/深度置信网络/故障诊断Key words
gear transmission/feature extraction/deep belief network (DBN)/fault diagnosis分类
机械制造引用本文复制引用
陈保家,刘浩涛,徐超,陈法法,肖文荣,赵春华..深度置信网络在齿轮故障诊断中的应用[J].中国机械工程,2019,30(2):205-211,7.基金项目
湖北省重点实验室开放基金资助项目(2018KJX03,2018KJX07,2018KJX08) （2018KJX03,2018KJX07,2018KJX08）
湖北省自然科学基金资助项目(2018CFB671) （2018CFB671）
湖北省质量技术监督局科技计划资助项目(Hbj-kj201714) （Hbj-kj201714）
