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.
Keywords
PrognosticsRolling-element bearingReliability (semiconductor)Bearing (navigation)VibrationEngineeringKernel (algebra)Reliability engineeringSupport vector machineConvergence (economics)Condition monitoringComputer scienceMachine learningArtificial intelligenceMathematics
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Publication Info
- Year
- 2018
- Type
- article
- Volume
- 69
- Issue
- 1
- Pages
- 401-412
- Citations
- 1539
- Access
- Closed
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Cite This
Biao Wang,
Yaguo Lei,
Naipeng Li
et al.
(2018).
A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings.
IEEE Transactions on Reliability
, 69
(1)
, 401-412.
https://doi.org/10.1109/tr.2018.2882682
Identifiers
- DOI
- 10.1109/tr.2018.2882682