Remaining useful life prediction of rolling element bearings based on health state assessment. (February 2016)

• Record Type: Journal Article
• Authors: Liu, Zhiliang; Zuo, Ming J; Qin, Yong
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 230:Number 2(2016)
• Page Start: 314
• Page End: 330
• Publication Date: 2016-02
• Subjects: Remaining useful life -- health state assessment -- support vector machine -- rolling element bearing -- accelerated degradation test; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/0954406215590167
• Languages: English
• ISSNs: 0954-4062

Abstract

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.