Data-physics-model based fatigue reliability assessment methodology for high-temperature components and its application in steam turbine rotor

Hang-Hang Gu, Run-Zi Wang, Min-Jin Tang, Xian-Cheng Zhang and Shan-Tung Tu

Reliability Engineering and System Safety, 2024, vol. 241, issue C

Abstract: This paper puts forward a data-physics-model based fatigue reliability assessment methodology, integrating monitoring data, physics of failure and stochastic process models, for high-temperature components. In detail, the monitored parameters are mapped into load spectrum assisted by the constructed surrogate, a physics-based health index is constructed through the damage accumulations considering uncertainties. Bayesian model averaging is utilized to combine three stochastic process models to evaluate the fatigue reliability. Furthermore, this methodology oriented to engineering application is thereafter implemented into steam turbine rotor, and the remaining useful life evaluation is presented to demonstrate the superiority of proposed method over the existing engineering method. It shows that an over conservative estimation can be avoided in application because the condition information is integrated into the fatigue modeling framework, which provides a reference to condition-based maintenance of high-temperature components.

Keywords: Data-physics-model; Fatigue reliability assessment; Steam turbine rotor; Bayesian model averaging (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023005471

DOI: 10.1016/j.ress.2023.109633

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