The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Direct Remaining Useful Life Estimation Based on Support Vector Regression
Abstract
Prognostics is a major activity in the field of prognostics and health management. It aims at increasing the reliability and safety of systems while reducing the maintenance cost by providing an estimate of the current health status and remaining useful life (RUL). Classical RUL estimation techniques are usually composed of different steps: estimations of a health indicator, degradation states, a failure threshold, and finally the RUL. In this work, a procedure that is able to estimate the RUL of equipment directly from sensor values without the need for estimating degradation states or a failure threshold is developed. A direct relation between sensor values or health indicators is modeled using a support vector regression. Using this procedure, the RUL can be estimated at any time instant of the degradation process. In addition, an offline wrapper variable selection is applied before training the prediction model. This step has a positive impact on the accuracy of the prediction while reducing the computational time compared to existing indirect RUL prediction methods. To assess the performance of the proposed approach, the Turbofan dataset, widely considered in the literature, is used. Experimental results show that the performance of the proposed method is competitive with other existing approaches.
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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
Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook
Arinez, J. F., Chang, Q., Gao, R. X., Xu, C., & Zhang, J. (2020) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4047855 · Cited by 584
Li, X., Zhang, L., Wang, Z., & Dong, P. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2018.12.011 · Cited by 445
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
Zhao, K., Jia, Z., Jia, F., & Shao, H. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105860 · Cited by 234
Metadata
- Authors
- Khelif, R., Chebel-Morello, B., Malinowski, S., Laajili, E., Fnaiech, F., & Zerhouni, N.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2017-03
- Volume
- 64
- Issue
- 3
- Pages
- 2276-2285
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2544905596
- Cited by
- 438
- References
- 37
- FWCI
- 23.85
- Type
- journal article
- Language
- en
- Open access
- closed
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- Khelif, R., Chebel-Morello, B., Malinowski, S., Laajili, E., Fnaiech, F., & Zerhouni, N. (2017). Direct Remaining Useful Life Estimation Based on Support Vector Regression. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2016.2623260
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