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Bayesian inference-based prognosis of fatigue damage for MPPO polymer using Zhurkov fatigue life model

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  • Jaehyeok Doh

Abstract

In this study, the fatigue damage prognosis of a modified polyphenylene oxide (MPPO) polymer is performed using a Bayesian framework, and a Zhurkov model-based dynamic fatigue life model is employed to obtain the probabilistic stress–cycle (P-S-N) curve. Activation energy and tensile tests are performed to determine the aleatory uncertainty of the lethargy coefficient of the Zhurkov fatigue life model. This uncertainty is quantified by performing sequential statistical modeling using experimental data with embedded scattering. The P-S-N curve is estimated using these data, and the Zhurkov fatigue life model is validated via the fatigue test. Furthermore, damage data are obtained via a low-cycle fatigue analysis in conditions identical to those of the fatigue test conducted on the specimen scale. Based on computational damage data, the initial model parameters of the fatigue damage model are obtained using the least-squares method. These model parameters are estimated while considering scattering by applying the Markov Chain Monte Carlo and particle filter. Therefore, the remaining useful life (RUL) of the MPPO, which depends on the amplitude stress, is predicted under the tension–tension fatigue loading ( R  = 0), and the prediction accuracy of the RUL is evaluated using prognostics metrics.

Suggested Citation

  • Jaehyeok Doh, 2023. "Bayesian inference-based prognosis of fatigue damage for MPPO polymer using Zhurkov fatigue life model," Journal of Risk and Reliability, , vol. 237(4), pages 636-653, August.
    • Handle: RePEc:sae:risrel:v:237:y:2023:i:4:p:636-653
    DOI: 10.1177/1748006X221132870
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    1. An, Dawn & Kim, Nam H. & Choi, Joo-Ho, 2015. "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 223-236.
    2. An, Dawn & Choi, Joo-Ho & Kim, Nam Ho, 2013. "Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 161-169.
    3. Zhu, Shun-Peng & Huang, Hong-Zhong & Peng, Weiwen & Wang, Hai-Kun & Mahadevan, Sankaran, 2016. "Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 1-12.
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