期刊
出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0954406215590167
关键词
Remaining useful life; health state assessment; support vector machine; rolling element bearing; accelerated degradation test
资金
- National Natural Science Foundation of China [51375078]
- State Key Laboratory of Rail Traffic Control and Safety [RCS2014K006]
- Beijing Jiaotong University
- China Postdoctoral Science Foundation [2015M570774]
- Natural Sciences and Engineering Research Council of Canada (NSERC)
Instead of looking for an overall regression model for remaining useful life (RUL) prediction, this paper proposes a RUL prediction framework based on multiple health state assessment that divides the entire bearing life into several health states where a local regression model can be built individually. A hybrid approach consisting of both unsupervised learning and supervised learning is proposed to automatically estimate the real-time health state of a bearing in cases with no prior knowledge available. Support vector machine is the main technology adopted to implement health state assessment and RUL prediction. Experimental results on accelerated degradation tests of rolling element bearings demonstrate the effectiveness of the proposed framework.
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