A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings
Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. Fir...
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| Published in | IEEE transactions on reliability Vol. 69; no. 1; pp. 401 - 412 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.03.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0018-9529 1558-1721 |
| DOI | 10.1109/TR.2018.2882682 |
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| Abstract | Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions with different kernel parameters. Then, exponential degradation models coupled with the Fréchet distance are employed to estimate the RUL adaptively. The proposed approach is evaluated using the vibration data from accelerated degradation tests of rolling element bearings and the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy and convergence of RUL prediction of rolling element bearings. |
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| AbstractList | Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions with different kernel parameters. Then, exponential degradation models coupled with the Fréchet distance are employed to estimate the RUL adaptively. The proposed approach is evaluated using the vibration data from accelerated degradation tests of rolling element bearings and the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy and convergence of RUL prediction of rolling element bearings. Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions with different kernel parameters. Then, exponential degradation models coupled with the Fréchet distance are employed to estimate the RUL adaptively. The proposed approach is evaluated using the vibration data from accelerated degradation tests of rolling element bearings and the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy and convergence of RUL prediction of rolling element bearings. |
| Author | Wang, Biao Lei, Yaguo Li, Ningbo Li, Naipeng |
| Author_xml | – sequence: 1 givenname: Biao orcidid: 0000-0003-4283-2211 surname: Wang fullname: Wang, Biao email: wangbiaoxjtu@outlook.com organization: Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, China – sequence: 2 givenname: Yaguo orcidid: 0000-0002-5167-1459 surname: Lei fullname: Lei, Yaguo email: yaguolei@mail.xjtu.edu.cn organization: Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, China – sequence: 3 givenname: Naipeng surname: Li fullname: Li, Naipeng email: li3112001096@stu.xjtu.edu.cn organization: Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, China – sequence: 4 givenname: Ningbo surname: Li fullname: Li, Ningbo email: liningbo1992@163.com organization: Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, China |
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| CODEN | IERQAD |
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| SubjectTerms | Accelerated tests Adaptation models Bearing degradation Bearings Data models Degradation Kernel Machine learning Predictive models prognostics relevance vector machine remaining useful life estimation Roller bearings Rolling bearings Support vector machines Useful life vibration monitoring |
| Title | A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings |
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