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A nonlinear mixed-effects model for degradation data obtained from in-service inspections

Author

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  • Yuan, X.-X.
  • Pandey, M.D.

Abstract

Monitoring of degradation and predicting its progression using periodic inspection data are important to ensure safety and reliability of engineering systems. Traditional regression models are inadequate in modeling the periodic inspection data, as it ignores units specific random effects and potential correlation among repeated measurements. This paper presents an advanced nonlinear mixed-effects (NLME) model, generally adopted in bio-statistical literature, for modeling and predicting degradation in nuclear piping system. The proposed model offers considerable improvement by reducing the variance associated with degradation of a specific unit, which leads to more realistic estimates of risk.

Suggested Citation

  • Yuan, X.-X. & Pandey, M.D., 2009. "A nonlinear mixed-effects model for degradation data obtained from in-service inspections," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 509-519.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:2:p:509-519
    DOI: 10.1016/j.ress.2008.06.013
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    Citations

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    Cited by:

    1. Guida, Maurizio & Postiglione, Fabio & Pulcini, Gianpaolo, 2015. "A random-effects model for long-term degradation analysis of solid oxide fuel cells," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 88-98.
    2. Hanzhong Liu & Jiacai Huang & Yuanhong Guan & Li Sun, 2019. "Accelerated Degradation Model of Nonlinear Wiener Process Based on Fixed Time Index," Mathematics, MDPI, vol. 7(5), pages 1-16, May.
    3. Andrade, Antonio Ramos & Stow, Julian, 2017. "Assessing the potential cost savings of introducing the maintenance option of ‘Economic Tyre Turning’ in Great Britain railway wheelsets," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 317-325.
    4. Yi Yang & John Dalsgaard Sørensen, 2019. "Cost-Optimal Maintenance Planning for Defects on Wind Turbine Blades," Energies, MDPI, vol. 12(6), pages 1-16, March.
    5. Wang, Pingping & Tang, Yincai & Joo Bae, Suk & He, Yong, 2018. "Bayesian analysis of two-phase degradation data based on change-point Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 244-256.
    6. Bui, Ha & Sakurahara, Tatsuya & Pence, Justin & Reihani, Seyed & Kee, Ernie & Mohaghegh, Zahra, 2019. "An algorithm for enhancing spatiotemporal resolution of probabilistic risk assessment to address emergent safety concerns in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 405-428.
    7. Jiang, R., 2010. "Optimization of alarm threshold and sequential inspection scheme," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 208-215.
    8. Si, Xiao-Sheng & Wang, Wenbin & Chen, Mao-Yin & Hu, Chang-Hua & Zhou, Dong-Hua, 2013. "A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution," European Journal of Operational Research, Elsevier, vol. 226(1), pages 53-66.
    9. Fallahdizcheh, Amirhossein & Wang, Chao, 2022. "Transfer learning of degradation modeling and prognosis based on multivariate functional analysis with heterogeneous sampling rates," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    10. Gao, Hongda & Cui, Lirong & Qiu, Qingan, 2019. "Reliability modeling for degradation-shock dependence systems with multiple species of shocks," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 133-143.
    11. Zhou, Shirong & Tang, Yincai & Xu, Ancha, 2021. "A generalized Wiener process with dependent degradation rate and volatility and time-varying mean-to-variance ratio," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    12. Sakurahara, Tatsuya & O'Shea, Nicholas & Cheng, Wen-Chi & Zhang, Sai & Reihani, Seyed & Kee, Ernie & Mohaghegh, Zahra, 2019. "Integrating renewal process modeling with Probabilistic Physics-of-Failure: Application to Loss of Coolant Accident (LOCA) frequency estimations in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.

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