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Application of stochastic filtering for lifetime prediction

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  • Myötyri, E.
  • Pulkkinen, U.
  • Simola, K.

Abstract

This paper introduces a stochastic filtering modeling approach for predicting the remaining lifetime of a component based on information on the stochastic degradation process and uncertain condition monitoring measurements. The model is illustrated by a case study, where the degradation is assumed to be a simplified fatigue crack growth process. The model accounts for uncertainties in both degradation process and condition measurements in a sound way. If completed with information on costs of monitoring, failure and replacement, such model could be used in optimizing both the condition monitoring intervals and, e.g. the replacement time for the component.

Suggested Citation

  • Myötyri, E. & Pulkkinen, U. & Simola, K., 2006. "Application of stochastic filtering for lifetime prediction," Reliability Engineering and System Safety, Elsevier, vol. 91(2), pages 200-208.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:2:p:200-208
    DOI: 10.1016/j.ress.2005.01.002
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    1. Christer, A. H. & Wang, W. & Sharp, J. M., 1997. "A state space condition monitoring model for furnace erosion prediction and replacement," European Journal of Operational Research, Elsevier, vol. 101(1), pages 1-14, August.
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    Cited by:

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    2. Pedregal, Diego J. & Carmen Carnero, Ma., 2009. "Vibration analysis diagnostics by continuous-time models: A case study," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 244-253.
    3. Zio, Enrico & Compare, Michele, 2013. "Evaluating maintenance policies by quantitative modeling and analysis," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 53-65.
    4. Baraldi, Piero & Mangili, Francesca & Zio, Enrico, 2013. "Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 94-108.
    5. Chiachío, Manuel & Chiachío, Juan & Sankararaman, Shankar & Goebel, Kai & Andrews, John, 2017. "A new algorithm for prognostics using Subset Simulation," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 189-199.
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    7. Cadini, F. & Zio, E. & Avram, D., 2009. "Model-based Monte Carlo state estimation for condition-based component replacement," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 752-758.
    8. Li, Yuan & Li, Jingwei & Wang, Huanjie & Liu, Chengbao & Tan, Jie, 2024. "Knowledge enhanced ensemble method for remaining useful life prediction under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    9. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
    10. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    11. Thanh Trung Le & Florent Chatelain & Christophe Bérenguer, 2016. "Multi-branch hidden Markov models for remaining useful life estimation of systems under multiple deterioration modes," Journal of Risk and Reliability, , vol. 230(5), pages 473-484, October.
    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. Hai-Kun Wang & Yan-Feng Li & Yu Liu & Yuan-Jian Yang & Hong-Zhong Huang, 2015. "Remaining useful life estimation under degradation and shock damage," Journal of Risk and Reliability, , vol. 229(3), pages 200-208, June.
    14. Fang, Xiaolei & Zhou, Rensheng & Gebraeel, Nagi, 2015. "An adaptive functional regression-based prognostic model for applications with missing data," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 266-274.
    15. Zio, Enrico & Peloni, Giovanni, 2011. "Particle filtering prognostic estimation of the remaining useful life of nonlinear components," Reliability Engineering and System Safety, Elsevier, vol. 96(3), pages 403-409.
    16. Chiachío, Juan & Chiachío, Manuel & Sankararaman, Shankar & Saxena, Abhinav & Goebel, Kai, 2015. "Condition-based prediction of time-dependent reliability in composites," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 134-147.
    17. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    18. Chiquet, Julien & Eid, Mohamed & Limnios, Nikolaos, 2008. "Modelling and estimating the reliability of stochastic dynamical systems with Markovian switching," Reliability Engineering and System Safety, Elsevier, vol. 93(12), pages 1801-1808.
    19. Ying Liao & Yisha Xiang & Min Wang, 2021. "Health assessment and prognostics based on higher‐order hidden semi‐Markov models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(2), pages 259-276, March.

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