期刊
IEEE TRANSACTIONS ON RELIABILITY
卷 65, 期 3, 页码 1314-1326出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TR.2016.2570568
关键词
Health indicator; parameter initialization; particle filtering; remaining useful life (RUL) prediction
类别
资金
- National Natural Science Foundation of China [51475355, 51421004]
- Young Talent Support Plan of Central Organization Department
- Fundamental Research Funds for the Central Universities [2012jdgz01, CXTD2014001]
Remaining useful life (RUL) prediction allows for predictive maintenance of machinery, thus reducing costly unscheduled maintenance. Therefore, RUL prediction of machinery appears to be a hot issue attracting more and more attention as well as being of great challenge. This paper proposes a model-based method for predicting RUL of machinery. The method includes two modules, i.e., indicator construction and RUL prediction. In the first module, a new health indicator named weighted minimum quantization error is constructed, which fuses mutual information from multiple features and properly correlates to the degradation processes of machinery. In the second module, model parameters are initialized using the maximum-likelihood estimation algorithm and RUL is predicted using a particle filtering-based algorithm. The proposed method is demonstrated using vibration signals from accelerated degradation tests of rolling element bearings. The prediction result identifies the effectiveness of the proposed method in predicting RUL of machinery.
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