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

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

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.

Suggested Citation

  • Gu, Hang-Hang & Wang, Run-Zi & Tang, Min-Jin & Zhang, Xian-Cheng & Tu, Shan-Tung, 2024. "Data-physics-model based fatigue reliability assessment methodology for high-temperature components and its application in steam turbine rotor," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023005471
    DOI: 10.1016/j.ress.2023.109633
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    1. Zang, Yu & Shangguan, Wei & Cai, Baigen & Wang, Huasheng & Pecht, Michael. G., 2021. "Hybrid remaining useful life prediction method. A case study on railway D-cables," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    2. Chen, Jian & Yuan, Shenfang & Sbarufatti, Claudio & Jin, Xin, 2021. "Dual crack growth prognosis by using a mixture proposal particle filter and on-line crack monitoring," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Keedy, Elias & Feng, Qianmei, 2012. "A physics-of-failure based reliability and maintenance modeling framework for stent deployment and operation," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 94-101.
    4. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.
    5. Wang, Xiaolin & Balakrishnan, Narayanaswamy & Guo, Bo, 2014. "Residual life estimation based on a generalized Wiener degradation process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 13-23.
    6. Gaspar, B. & Teixeira, A.P. & Guedes Soares, C., 2017. "Adaptive surrogate model with active refinement combining Kriging and a trust region method," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 277-291.
    7. Kostandyan, Erik E. & Sørensen, John D., 2012. "Physics of failure as a basis for solder elements reliability assessment in wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 100-107.
    8. Park, Inseok & Grandhi, Ramana V., 2014. "A Bayesian statistical method for quantifying model form uncertainty and two model combination methods," Reliability Engineering and System Safety, Elsevier, vol. 129(C), pages 46-56.
    9. Yeratapally, Saikumar R. & Glavicic, Michael G. & Argyrakis, Christos & Sangid, Michael D., 2017. "Bayesian uncertainty quantification and propagation for validation of a microstructure sensitive model for prediction of fatigue crack initiation," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 110-123.
    10. Shi, Zunya & Chehade, Abdallah, 2021. "A dual-LSTM framework combining change point detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    11. Liu, Di & Wang, Shaoping & Zhang, Chao & Tomovic, Mileta, 2018. "Bayesian model averaging based reliability analysis method for monotonic degradation dataset based on inverse Gaussian process and Gamma process," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 25-38.
    12. Liu, Yingchao & Hu, Xiaofeng & Zhang, Wenjuan, 2019. "Remaining useful life prediction based on health index similarity," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 502-510.
    13. Andrew Gelman, 2011. "Induction and Deduction in Baysian Data Analysis," Rationality, Markets and Morals, Frankfurt School Verlag, Frankfurt School of Finance & Management, vol. 2(43), September.
    14. Bichon, Barron J. & McFarland, John M. & Mahadevan, Sankaran, 2011. "Efficient surrogate models for reliability analysis of systems with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1386-1395.
    15. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Fang, Xiaolei & Cai, Xiao & Yan, Tao, 2021. "Remaining useful life prediction based on a multi-sensor data fusion model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    16. Xiao-Sheng Si & Zheng-Xin Zhang & Chang-Hua Hu, 2017. "Data-Driven Remaining Useful Life Prognosis Techniques," Springer Series in Reliability Engineering, Springer, number 978-3-662-54030-5, September.
    17. Xu, Xiaodong & Tang, Shengjin & Yu, Chuanqiang & Xie, Jian & Han, Xuebing & Ouyang, Minggao, 2021. "Remaining Useful Life Prediction of Lithium-ion Batteries Based on Wiener Process Under Time-Varying Temperature Condition," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    18. Gao, Haifeng & Wang, Anjenq & Zio, Enrico & Bai, Guangchen, 2020. "An integrated reliability approach with improved importance sampling for low-cycle fatigue damage prediction of turbine disks," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    19. Si, Xiao-Sheng & Li, Tianmei & Zhang, Jianxun & Lei, Yaguo, 2022. "Nonlinear degradation modeling and prognostics: A Box-Cox transformation perspective," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    20. Shittu, Abdulhakim Adeoye & Mehmanparast, Ali & Hart, Phil & Kolios, Athanasios, 2021. "Comparative study between S-N and fracture mechanics approach on reliability assessment of offshore wind turbine jacket foundations," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    21. Wang, Run-Zi & Gu, Hang-Hang & Zhu, Shun-Peng & Li, Kai-Shang & Wang, Ji & Wang, Xiao-Wei & Hideo, Miura & Zhang, Xian-Cheng & Tu, Shan-Tung, 2022. "A data-driven roadmap for creep-fatigue reliability assessment and its implementation in low-pressure turbine disk at elevated temperatures," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
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