中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Abnormal State Detection for Rolling Bearings Using DBSE Approach

文献类型:会议论文

作者Gui J(桂珺)1,2,3,4; Zheng ZY(郑泽宇)1,4; Zhao XF(赵雪峰)1,4; Qin ZB(秦兆伯)3
出版日期2019
会议日期December 6-9, 2019
会议地点Chengdu, China
关键词rolling bearing fault diagnosis distribution-based states extraction
页码1366-1370
英文摘要Rolling bearings play an important role in mechanical equipment. Abnormal state detection of rolling bearings is significant to avoid industrial accident. In this paper, we use Distribution-based States Extraction (DBSE) approach to detect and analyze the abnormal state of rolling bearings. We apply DBSE approach on three different datasets. Each dataset contains time series of horizon acceleration and vertical acceleration right before a specific fault of a rolling bearing. The results show that the DBSE is able to extract normal states and abnormal states before all three faults and effective for fault prediction. By analyzing statistical properties of two states for all three faults, we find distributions of acceleration data on two states are quite different. Further more, we find that standard deviations are more effective to describe features of different states than means of acceleration data. Our finding are great useful for abnormal detection and fault prediction.
产权排序1
会议录2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019
会议录出版者IEEE
会议录出版地NEW YORK
语种英语
ISBN号978-1-7281-4743-7
源URL[http://ir.sia.cn/handle/173321/26756]  
专题沈阳自动化研究所_数字工厂研究室
通讯作者Gui J(桂珺)
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China
4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
推荐引用方式
GB/T 7714
Gui J,Zheng ZY,Zhao XF,et al. Abnormal State Detection for Rolling Bearings Using DBSE Approach[C]. 见:. Chengdu, China. December 6-9, 2019.

入库方式: OAI收割

来源:沈阳自动化研究所

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