The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings
Abstract
Condition-based maintenance, which includes both diagnosis and prognosis of faults, is a topic of growing interest for improving the reliability of electrical drives. Bearings constitute a large portion of failures in rotational machines. Although many techniques have been successfully applied for bearing fault diagnosis, prognosis of faults, particularly predicting the remaining useful life (RUL) of bearings, is a remaining challenge. The main reasons for this are a lack of accurate physical degradation models and limited labeled training data. In this paper, we introduce a data-driven methodology, which relies on both time and time-frequency domain features to track the evolution of bearing faults. Once features are extracted, an analytical function that best approximates the evolution of the fault is determined and used to learn the parameters of an extended Kalman filter (KF). The learned extended KF is applied to testing data to predict the RUL of bearing faults under different operating conditions. The performance of the proposed method is evaluated on PRONOSTIA experimental testbed data.
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Cites
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On Measures of Entropy and Information
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Remaining useful life estimation – A review on the statistical data driven approaches
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Advances in Diagnostic Techniques for Induction Machines
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PRONOSTIA : An experimental platform for bearings accelerated degradation tests.
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel‐Morello, B., Noureddine Zerhouni, A., & Varnier, C. (2012) · HAL · Cited by 928
Prognostics and Health Management of Electronics
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Cited by
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.016 · Cited by 2,634
A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings
Wang, B., Lei, Y., Li, N., & Li, N. (2020) · IEEE Transactions on Reliability · doi:10.1109/tr.2018.2882682 · Cited by 1,917
Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture
Yang, B., Liu, R., & Zio, E. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2924605 · Cited by 429
A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem
Wang, Y., Peng, Y., Zi, Y., Jin, X., & Tsui, K.-L. (2016) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2016.2535368 · Cited by 344
A comparison of methodologies for the non-invasive characterisation of commercial Li-ion cells
Barai, A., Uddin, K., Dubarry, M., Somerville, L., McGordon, A., Jennings, P., & Bloom, I. (2019) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2019.01.001 · Cited by 319
Metadata
- Authors
- Singleton, R. K., Strangas, E. G., & Aviyente, S.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2015-03
- Volume
- 62
- Issue
- 3
- Pages
- 1781-1790
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2142724780
- Cited by
- 364
- References
- 44
- FWCI
- 18.61
- Type
- journal article
- Language
- en
- Open access
- closed
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- Singleton, R. K., Strangas, E. G., & Aviyente, S. (2015). Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2014.2336616
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