IDEAS home Printed from https://ideas.repec.org/a/taf/uiiexx/v45y2013i9p939-952.html
   My bibliography  Save this article

Condition monitoring and remaining useful life prediction using degradation signals: revisited

Author

Listed:
  • Nan Chen
  • Kwok Tsui

Abstract

Condition monitoring is an important prognostic tool to determine the current operation status of a system/device and to estimate the distribution of the remaining useful life. This article proposes a two-phase model to characterize the degradation process of rotational bearings. A Bayesian framework is used to integrate historical data with up-to-date in situ observations of new working units to improve the degradation modeling and prediction. A new approach is developed to compute the distribution of the remaining useful life based on the degradation signals, which is more accurate compared with methods reported in the literature. Finally, extensive numerical results demonstrate that the proposed framework is effective and efficient.

Suggested Citation

  • Nan Chen & Kwok Tsui, 2013. "Condition monitoring and remaining useful life prediction using degradation signals: revisited," IISE Transactions, Taylor & Francis Journals, vol. 45(9), pages 939-952.
    • Handle: RePEc:taf:uiiexx:v:45:y:2013:i:9:p:939-952
    DOI: 10.1080/0740817X.2012.706376
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/0740817X.2012.706376
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/0740817X.2012.706376?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.

    Citations

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


    Cited by:

    1. Yang, Li & Zhao, Yu & Peng, Rui & Ma, Xiaobing, 2018. "Hybrid preventive maintenance of competing failures under random environment," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 130-140.
    2. Shi, Yue & Zhu, Weihang & Xiang, Yisha & Feng, Qianmei, 2020. "Condition-based maintenance optimization for multi-component systems subject to a system reliability requirement," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    3. Son, Junbo & Zhou, Shiyu & Sankavaram, Chaitanya & Du, Xinyu & Zhang, Yilu, 2016. "Remaining useful life prediction based on noisy condition monitoring signals using constrained Kalman filter," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 38-50.
    4. Andrey A. Radionov & Ivan V. Liubimov & Igor M. Yachikov & Ildar R. Abdulveleev & Ekaterina A. Khramshina & Alexander S. Karandaev, 2023. "Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data," Energies, MDPI, vol. 16(12), pages 1-27, June.
    5. Abdenour Soualhi & Mourad Lamraoui & Bilal Elyousfi & Hubert Razik, 2022. "PHM SURVEY: Implementation of Prognostic Methods for Monitoring Industrial Systems," Energies, MDPI, vol. 15(19), pages 1-24, September.
    6. Prashant Kumar & Salman Khalid & Heung Soo Kim, 2023. "Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review," Mathematics, MDPI, vol. 11(13), pages 1-37, July.
    7. Ling, M.H. & Ng, H.K.T. & Tsui, K.L., 2019. "Bayesian and likelihood inferences on remaining useful life in two-phase degradation models under gamma process," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 77-85.
    8. Gao, Hongda & Cui, Lirong & Dong, Qinglai, 2020. "Reliability modeling for a two-phase degradation system with a change point based on a Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    9. Deep, Akash & Zhou, Shiyu & Veeramani, Dharmaraj & Chen, Yong, 2023. "Partially observable Markov decision process-based optimal maintenance planning with time-dependent observations," European Journal of Operational Research, Elsevier, vol. 311(2), pages 533-544.
    10. Junyu Guo & Hong-Zhong Huang & Weiwen Peng & Jie Zhou, 2019. "Bayesian information fusion for degradation analysis of deteriorating products with individual heterogeneity," Journal of Risk and Reliability, , vol. 233(4), pages 615-622, August.
    11. Shu, Yin & Feng, Qianmei & Liu, Hao, 2019. "Using degradation-with-jump measures to estimate life characteristics of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    12. Wen, Yuxin & Wu, Jianguo & Das, Devashish & Tseng, Tzu-Liang(Bill), 2018. "Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 113-124.
    13. Chen, Zhen & Li, Yaping & Zhou, Di & Xia, Tangbin & Pan, Ershun, 2021. "Two-phase degradation data analysis with change-point detection based on Gaussian process degradation model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    14. de Pater, Ingeborg & Mitici, Mihaela, 2021. "Predictive maintenance for multi-component systems of repairables with Remaining-Useful-Life prognostics and a limited stock of spare components," Reliability Engineering and System Safety, Elsevier, vol. 214(C).

    More about this item

    Statistics

    Access and download statistics

    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:taf:uiiexx:v:45:y:2013:i:9:p:939-952. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/uiie .

    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.