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Data-Driven Remaining Useful Life Prognosis Techniques

Author

Listed:
  • Xiao-Sheng Si

    (Xi’an Institute of High-Technology)

  • Zheng-Xin Zhang

    (Xi’an Institute of High-Technology Department of Automation)

  • Chang-Hua Hu

    (Xi’an Institute of High-Technology)

Abstract

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Suggested Citation

  • Xiao-Sheng Si & Zheng-Xin Zhang & Chang-Hua Hu, 2017. "Data-Driven Remaining Useful Life Prognosis Techniques," Springer Series in Reliability Engineering, Springer, number 978-3-662-54030-5, March.
    • Handle: RePEc:spr:ssreng:978-3-662-54030-5
    DOI: 10.1007/978-3-662-54030-5
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    Cited by:

    1. Gu, Hang-Hang & Wang, Run-Zi & Tang, Min-Jin & Zhang, Xian-Cheng & Tu, Shan-Tung, 2024. "Data-physics-model based fatigue reliability assessment methodology for high-temperature components and its application in steam turbine rotor," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Jürgen Herp & Niels L. Pedersen & Esmaeil S. Nadimi, 2019. "Assessment of Early Stopping through Statistical Health Prognostic Models for Empirical RUL Estimation in Wind Turbine Main Bearing Failure Monitoring," Energies, MDPI, vol. 13(1), pages 1-18, December.
    3. Van der Auweraer, Sarah & Boute, Robert, 2019. "Forecasting spare part demand using service maintenance information," International Journal of Production Economics, Elsevier, vol. 213(C), pages 138-149.
    4. Eleftheroglou, Nick & Galanopoulos, Georgios & Loutas, Theodoros, 2024. "Similarity learning hidden semi-Markov model for adaptive prognostics of composite structures," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    5. Benedikt Wiese & Niels L. Pedersen & Esmaeil S. Nadimi & Jürgen Herp, 2020. "Estimating the Remaining Power Generation of Wind Turbines—An Exploratory Study for Main Bearing Failures," Energies, MDPI, vol. 13(13), pages 1-11, July.
    6. Van der Auweraer, Sarah & Boute, Robert N. & Syntetos, Aris A., 2019. "Forecasting spare part demand with installed base information: A review," International Journal of Forecasting, Elsevier, vol. 35(1), pages 181-196.

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