IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v9y2018i5d10.1007_s13198-018-0708-y.html
   My bibliography  Save this article

A multi-indicator modeling method for similarity-based residual useful life estimation with two selection processes

Author

Listed:
  • Mengyao Gu

    (Chongqing University)

  • Youling Chen

    (Chongqing University)

Abstract

The similarity-based residual useful life prediction (SbRLP) approach, a burgeoning technique, plays an increasingly important role in the residual useful life (RUL) prediction. Currently, studies on taking (a) the selection of degradation indicatorsf; and (b) the selection of reference samples into account are rare. While they have positive influence on improving the performance of the SbRLP method with multiple degradation indicator (MSbRLP). Driven by the abovementioned gap, two novel selection processes are advanced by this article and thus an improved MSbRLP method is developed. Then, in case study of the gyroscope RUL estimation, two selection processes and the improved MSbRLP method are illustrated to be reasonable and effective by comparison with the classical MSbRLP method. Furthermore, implementing results reveal that the existence of degradation indicator having bad reflection effect will truly increase the recognition error of the degradation state and hence decrease the accuracy of RUL estimation.

Suggested Citation

  • Mengyao Gu & Youling Chen, 2018. "A multi-indicator modeling method for similarity-based residual useful life estimation with two selection processes," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(5), pages 987-998, October.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:5:d:10.1007_s13198-018-0708-y
    DOI: 10.1007/s13198-018-0708-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-018-0708-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-018-0708-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. A. Mosallam & K. Medjaher & N. Zerhouni, 2016. "Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1037-1048, October.
    2. Zio, Enrico & Di Maio, Francesco, 2010. "A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 49-57.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nikhil M. Thoppil & V. Vasu & C. S. P. Rao, 2021. "Health indicator construction and remaining useful life estimation for mechanical systems using vibration signal prognostics," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(5), pages 1001-1010, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcin Witczak & Marcin Mrugalski & Bogdan Lipiec, 2021. "Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework," Energies, MDPI, vol. 14(8), pages 1-23, April.
    2. Jie Yang & Shaowen Lu & Liangyong Wang, 2020. "Fused magnesia manufacturing process: a survey," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 327-350, February.
    3. Merainani, Boualem & Laddada, Sofiane & Bechhoefer, Eric & Chikh, Mohamed Abdessamed Ait & Benazzouz, Djamel, 2022. "An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples," Renewable Energy, Elsevier, vol. 182(C), pages 1141-1151.
    4. García Nieto, P.J. & García-Gonzalo, E. & Sánchez Lasheras, F. & de Cos Juez, F.J., 2015. "Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 219-231.
    5. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Gwan-Soo Park & Hee-Je Kim, 2019. "Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features," Energies, MDPI, vol. 12(22), pages 1-14, November.
    7. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne & Levrat, Eric & Iung, Benoît, 2013. "Remaining useful life estimation based on stochastic deterioration models: A comparative study," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 165-175.
    8. Xiaojia Wang & Ting Huang & Keyu Zhu & Xibin Zhao, 2022. "LSTM-Based Broad Learning System for Remaining Useful Life Prediction," Mathematics, MDPI, vol. 10(12), pages 1-13, June.
    9. Vega, Manuel A. & Hu, Zhen & Todd, Michael D., 2020. "Optimal maintenance decisions for deteriorating quoin blocks in miter gates subject to uncertainty in the condition rating protocol," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    10. Bellaera, R. & Bonifetto, R. & Di Maio, F. & Pedroni, N. & Savoldi, L. & Zanino, R. & Zio, E., 2020. "Integrated deterministic and probabilistic safety assessment of a superconducting magnet cryogenic cooling circuit for nuclear fusion applications," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    11. Hua-Xi Zhou & Chang-Guang Zhou & Hu-Tian Feng, 2023. "An integrated lifetime prediction method for double-nut ball screws subject to preload loss failure mode," Journal of Risk and Reliability, , vol. 237(6), pages 1248-1258, December.
    12. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
    13. Pai Zheng & Xun Xu & Chun-Hsien Chen, 2020. "A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 3-18, January.
    14. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    15. Zhiguo Zeng & Francesco Di Maio & Enrico Zio & Rui Kang, 2017. "A hierarchical decision-making framework for the assessment of the prediction capability of prognostic methods," Journal of Risk and Reliability, , vol. 231(1), pages 36-52, February.
    16. Riku-Pekka Nikula & Konsta Karioja & Kauko Leiviskä & Esko Juuso, 2019. "Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1563-1579, April.
    17. Xi, Zhimin & Jing, Rong & Wang, Pingfeng & Hu, Chao, 2014. "A copula-based sampling method for data-driven prognostics," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 72-82.
    18. Li, Mingxin & Jiang, Xiaoli & Carroll, James & Negenborn, Rudy R., 2022. "A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty," Applied Energy, Elsevier, vol. 321(C).
    19. Thi-Tinh Le & Seok-Ju Lee & Minh-Chau Dinh & Minwon Park, 2023. "Design of an Improved Remaining Useful Life Prediction Model Based on Vibration Signals of Wind Turbine Rotating Components," Energies, MDPI, vol. 17(1), pages 1-18, December.
    20. Nguyen, Khanh T.P. & Medjaher, Kamal, 2019. "A new dynamic predictive maintenance framework using deep learning for failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 251-262.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:9:y:2018:i:5:d:10.1007_s13198-018-0708-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.