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Degradation modeling for real-time estimation of residual lifetimes in dynamic environments

Author

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  • Linkan Bian
  • Nagi Gebraeel
  • Jeffrey P. Kharoufeh

Abstract

This article presents a methodology for modeling degradation signals from components functioning under dynamically evolving environment conditions. In situ sensor signals related to the degradation process are utilized as well as the environment conditions, to predict and update, in real-time, the distribution of a component’s residual lifetime. The model assumes that the time-dependent rate at which a component’s degradation signal increases (or decreases) is affected by the severity of the current environmental or operational conditions. These conditions are assumed to evolve as a continuous-time Markov chain. Unique to the proposed model is the union of historical data with real-time, sensor-based data to update the signal parameters, environment parameters, and the residual lifetime distribution of the component within a Bayesian framework.

Suggested Citation

  • Linkan Bian & Nagi Gebraeel & Jeffrey P. Kharoufeh, 2015. "Degradation modeling for real-time estimation of residual lifetimes in dynamic environments," IISE Transactions, Taylor & Francis Journals, vol. 47(5), pages 471-486, May.
    • Handle: RePEc:taf:uiiexx:v:47:y:2015:i:5:p:471-486
    DOI: 10.1080/0740817X.2014.955153
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    Citations

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    Cited by:

    1. Xu, Huyang & Fard, Nasser & Fang, Yuanchen, 2020. "Time series chain graph for modeling reliability covariates in degradation process," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    2. Wang, Xiaofei & Wang, Bing Xing & Jiang, Pei Hua & Hong, Yili, 2020. "Accurate reliability inference based on Wiener process with random effects for degradation data," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    3. Tao, Tao & Zio, Enrico & Zhao, Wei, 2018. "A novel support vector regression method for online reliability prediction under multi-state varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 35-49.
    4. Long, Junqi & Chen, Chuanhai & Liu, Zhifeng & Guo, Jinyan & Chen, Weizheng, 2022. "Stochastic hybrid system approach to task-orientated remaining useful life prediction under time-varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. Jingyuan Shen & Lirong Cui, 2017. "Reliability performance for dynamic multi-state repairable systems with regimes," IISE Transactions, Taylor & Francis Journals, vol. 49(9), pages 911-926, September.
    6. 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.
    7. Thirupathi Samala & Vijaya Kumar Manupati & Maria Leonilde R. Varela & Goran Putnik, 2021. "Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review," Future Internet, MDPI, vol. 13(3), pages 1-18, February.
    8. Thirupathi Samala & Vijaya Kumar Manupati & Bethalam Brahma Sai Nikhilesh & Maria Leonilde Rocha Varela & Goran Putnik, 2021. "Job Adjustment Strategy for Predictive Maintenance in Semi-Fully Flexible Systems Based on Machine Health Status," Sustainability, MDPI, vol. 13(9), pages 1-20, May.
    9. Mo, Huadong & Sansavini, Giovanni, 2019. "Impact of aging and performance degradation on the operational costs of distributed generation systems," Renewable Energy, Elsevier, vol. 143(C), pages 426-439.
    10. Sun, Xuxue & Mraied, Hesham & Cai, Wenjun & Zhang, Qiong & Liang, Guoyuan & Li, Mingyang, 2018. "Bayesian latent degradation performance modeling and quantification of corroding aluminum alloys," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 84-96.
    11. 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.
    12. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Bian, Linkan & Si, Xiaosheng, 2019. "Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 88-100.
    13. Peihua Jiang, 2022. "Statistical Inference of Wiener Constant-Stress Accelerated Degradation Model with Random Effects," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    14. Liu, Yu & Liu, Qinzhen & Xie, Chaoyang & Wei, Fayuan, 2019. "Reliability assessment for multi-state systems with state transition dependency," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 276-288.

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