Remaining useful life estimation based on Wiener degradation processes with random failure threshold
Remaining useful life (RUL) estimation based on condition monitoring data is central to condition based maintenance (CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the failure threshold has not been studied thoroughly. In this work, by using the truncat...
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| Published in | Journal of Central South University Vol. 23; no. 9; pp. 2230 - 2241 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Changsha
Central South University
01.09.2016
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2095-2899 2227-5223 |
| DOI | 10.1007/s11771-016-3281-z |
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| Abstract | Remaining useful life (RUL) estimation based on condition monitoring data is central to condition based maintenance (CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the failure threshold has not been studied thoroughly. In this work, by using the truncated normal distribution to model random failure threshold (RFT), an analytical and closed-form RUL distribution based on the current observed data was derived considering the posterior distribution of the drift parameter. Then, the Bayesian method was used to update the prior estimation of failure threshold. To solve the uncertainty of the censored in situ data of failure threshold, the expectation maximization (EM) algorithm is used to calculate the posteriori estimation of failure threshold. Numerical examples show that considering the randomness of the failure threshold and updating the prior information of RFT could improve the accuracy of real time RUL estimation. |
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| AbstractList | Remaining useful life (RUL) estimation based on condition monitoring data is central to condition based maintenance (CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the failure threshold has not been studied thoroughly. In this work, by using the truncated normal distribution to model random failure threshold (RFT), an analytical and closed-form RUL distribution based on the current observed data was derived considering the posterior distribution of the drift parameter. Then, the Bayesian method was used to update the prior estimation of failure threshold. To solve the uncertainty of the censored in situ data of failure threshold, the expectation maximization (EM) algorithm is used to calculate the posteriori estimation of failure threshold. Numerical examples show that considering the randomness of the failure threshold and updating the prior information of RFT could improve the accuracy of real time RUL estimation. |
| Author | Tang, Sheng-jin Feng, Yong-bao Yu, Chuan-qiang Xie, Jian Gao, Qin-he Si, Xiao-sheng |
| Author_xml | – sequence: 1 givenname: Sheng-jin surname: Tang fullname: Tang, Sheng-jin organization: High-Tech Institute of Xi’an – sequence: 2 givenname: Chuan-qiang surname: Yu fullname: Yu, Chuan-qiang email: yuchuanqiang201@126.com organization: High-Tech Institute of Xi’an – sequence: 3 givenname: Yong-bao surname: Feng fullname: Feng, Yong-bao organization: High-Tech Institute of Xi’an – sequence: 4 givenname: Jian surname: Xie fullname: Xie, Jian organization: High-Tech Institute of Xi’an – sequence: 5 givenname: Qin-he surname: Gao fullname: Gao, Qin-he organization: High-Tech Institute of Xi’an – sequence: 6 givenname: Xiao-sheng surname: Si fullname: Si, Xiao-sheng organization: High-Tech Institute of Xi’an |
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| Keywords | wiener process bayesian random failure threshold remaining useful life condition based maintenance EM algorithm |
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| Snippet | Remaining useful life (RUL) estimation based on condition monitoring data is central to condition based maintenance (CBM). In the current methods about the... |
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| SubjectTerms | Bayesian analysis Condition monitoring Control Science and Information Engineering Current distribution Engineering Failure Mathematical models Mechanical Engineering Metallic Materials Normal distribution Randomness Useful life |
| Title | Remaining useful life estimation based on Wiener degradation processes with random failure threshold |
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