当前位置: X-MOL 学术Measurement › 论文详情
A two-stage method for bearing fault detection using graph similarity evaluation
Measurement ( IF 3.364 ) Pub Date : 2020-06-27 , DOI: 10.1016/j.measurement.2020.108138
Weifang Sun; Yuqing Zhou; Xincheng Cao; Binqiang Chen; Wei Feng; Leiqing Chen

Robust identification of bearing health states is closely linked to timely condition monitoring and downtime reducing for rotating machinery. Although many proposed algorithms achieve extraordinary performances on feature extraction, uncertainty still remains for the bearing fault identification. To address this problem, this paper introduces a two-stage framework for bearing fault detection using graph similarity evaluation. The recognition stage is used to identify the operation state (fault or not) based on an improved graph-based method according to the sampled vibration signal for each spindle turn. The feature extraction stage, on the other hand, is implemented to extract the fault characters from the time-domain signals. The results indicate that the proposed method achieves 100% identification accuracy in bearing fault detection even with phase shifts. This work therefore provides a powerful tool for bearing faults detection and is broadly applicable to a variety of engineering applications and experimental conditions.
更新日期:2020-07-03

 

全部期刊列表>>
材料学研究精选
Springer Nature Live 产业与创新线上学术论坛
胸腔和胸部成像专题
自然科研论文编辑服务
ACS ES&T Engineering
ACS ES&T Water
屿渡论文,编辑服务
杨超勇
周一歌
华东师范大学
段炼
清华大学
中科大
唐勇
跟Nature、Science文章学绘图
隐藏1h前已浏览文章
中洪博元
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
x-mol收录
福州大学
南京大学
王杰
左智伟
电子显微学
何凤
洛杉矶分校
吴杰
赵延川
试剂库存
天合科研
down
wechat
bug