Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 134, Issue -, Pages -Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2019.106333
Keywords
Double-Wiener process; Multiple degradation feature extraction and selection; Reliability prediction; Monte Carlo algorithm
Categories
Funding
- National Natural Science Foundation of China [51875225, 51605095]
- National Key Research and Development Program of China [2018YFB1702302]
- Key Research and Development Program of Guangdong Province, China [2019B090916001]
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Reliability prediction is of great importance to improve the operational safety of machinery and decreasing their maintenance costs. In this paper, a new method combining double-Wiener process model with Monte Carlo algorithm is proposed to solve the problem of degradation modeling and reliability prediction of machinery with multiple degradation characteristics. Unlike other Wiener process-based prediction methods that only the mean is modelled, this method considers the degradation of both mean and variance, making degradation modelling more accurate. Meanwhile, the proposed method estimates reliability level of the machinery based on its entire monitoring information to date through an expectation maximization algorithm and a Monte Carlo algorithm, which does not require historical degradation data of other machines in a population. A numerical degradation case and a bearing degradation case with a set of degradation data are studied to validate the proposed method. The results demonstrate the effectiveness of the proposed method compared with other existing methods. (C) 2019 Elsevier Ltd. All rights reserved.
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