Degradation modeling using Bayesian hierarchical piecewise linear models: A case study to predict void swelling in irradiated materials

References

• Bonilla, E. V., K. Chai, and C. Williams. 2007. Multi-task Gaussian process prediction. Advances in Neural Information Processing Systems 20.
   Google Scholar
• Breiman, L. 2001. Random forests. Machine Learning 45 (1):5–32. doi: 10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Browne, W. J., and D. Draper. 2006. A comparison of Bayesian and likelihood-based methods for fitting multilevel models. Bayesian Analysis 1 (3):473–514. doi: 10.1214/06-BA117.
   Web of Science ®Google Scholar
• Bürkner, P.-C. 2017. Brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software 80 (1):1–28. doi: 10.18637/jss.v080.i01.
   Web of Science ®Google Scholar
• Dietterich, T. G. 2000. Ensemble methods in machine learning. In International workshop on multiple classifier systems, 1–15. Berlin, Heidelberg: Springer Berlin Heidelberg.
   Google Scholar
• Friedman, J. H. 2001. Greedy function approximation: A gradient boosting machine. Annals of Statistics 29 (5): 1189–232.
   Web of Science ®Google Scholar
• Garner, F. A. 1994. Irradiation performance of cladding and structural steels in liquid metal reactors. Materials Science and Technology: A Comprehensive Treatment 10 (October):419–543.
   Google Scholar
• Gelman, A. 2006. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Analysis 1 (3):515–34. doi: 10.1214/06-BA117A.
   Web of Science ®Google Scholar
• Gray, C. S., and S. J. Watson. 2010. Physics of failure approach to wind turbine condition based maintenance. Wind Energy 13 (5):395–405. doi: 10.1002/we.360.
   Web of Science ®Google Scholar
• Hoffman, M. D., and A. Gelman. 2014. The no-U-turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research 15 (1):1593–1623. http://mcmc-jags.sourceforge.net.
   Web of Science ®Google Scholar
• Huberts, L. C. E., M. Schoonhoven, and R. J. M. M. Does. 2022. Multilevel process monitoring: A case study to predict student success or failure. Journal of Quality Technology 54 (2):127–43. doi: 10.1080/00224065.2020.1828008.
   Web of Science ®Google Scholar
• Hudson, J. A. 1976. Void formation in solution-treated AISI 316 and 321 stainless steels under 46.5 Mev Ni6+ irradiation. Journal of Nuclear Materials 60 (1):89–106. doi: 10.1016/0022-3115(76)90121-5.
   Web of Science ®Google Scholar
• Igata, N., Y. Kohno, N. Tanabe, F. Rotman, and H. Tsunakawa. 1984. Effects of nitrogen and carbon on void swelling in austenitic stainless steels. Journal of Nuclear Materials 122 (1–3):219–23. doi: 10.1016/0022-3115(84)90599-3.
   Web of Science ®Google Scholar
• Jin, M., P. Cao, and M. P. Short. 2019. Predicting the onset of void swelling in irradiated metals with machine learning. Journal of Nuclear Materials 523 (September):189–97. doi: 10.1016/j.jnucmat.2019.05.054.
   Google Scholar
• Joe, H. 2006. Generating random correlation matrices based on partial correlations. Journal of Multivariate Analysis 97 (10):2177–89. doi: 10.1016/j.jmva.2005.05.010.
   Web of Science ®Google Scholar
• Kalchenko, A. S., V. V. Bryk, N. P. Lazarev, I. M. Neklyudov, V. N. Voyevodin, and F. A. Garner. 2010. Prediction of swelling of 18Cr10NiTi austenitic steel over a wide range of displacement rates. Journal of Nuclear Materials 399 (1):114–21. doi: 10.1016/j.jnucmat.2010.01.010.
   Web of Science ®Google Scholar
• Kim, M., and K. Liu. 2020. A Bayesian deep learning framework for interval estimation of remaining useful life in complex systems by incorporating general degradation characteristics. IISE Transactions 53 (3):326–340. doi: 10.1080/24725854.2020.1766729.
   Web of Science ®Google Scholar
• Konings, R. J. M., and R. Stoller. 2020. Comprehensive nuclear materials. Amsterdam, Netherlands: Elsevier.
   Google Scholar
• LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. Nature 521 (7553):436–44. doi: 10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• Li, Y., S. Hu, X. Sun, F. Gao, C. H. Henager, and M. Khaleel. 2011. Phase-field modeling of void evolution and swelling in materials under irradiation. Science China: Physics, Mechanics and Astronomy 54:856–65. doi: 10.1007/s11433-011-4316-y.
   Web of Science ®Google Scholar
• Liu, K., and J. Shi. 2015. IoT-enabled system informatics for service decision making. IEEE Intelligent Systems 30 (6):18–21.
   Web of Science ®Google Scholar
• Lu, C. J., and W. Q. Meeker. 1993. Using degradation measures to estimate a time-to-failure distribution. Technometrics 35 (2):161–74. doi: 10.2307/1269661.
   Web of Science ®Google Scholar
• Oppenheimer, C. H., and K. A. Loparo. 2002. Physically based diagnosis and prognosis of cracked rotor shafts. In Component and systems diagnostics, prognostics, and health management II, vol. 4733, 122.32. Bellingham, WA: SPIE.
   Google Scholar
• Stan Development Team. 2022. “Stan Modeling Language Users Guide and Reference Manual, Version 2.30.” https://mc-stan.org.
   Google Scholar
• Stiratelli, R., N. Laird, and J. H. Ware. 1984. Random-effects models for serial observations with binary response. Biometrics 40 (4):961–71. doi: 10.2307/2531147.
   PubMed Web of Science ®Google Scholar
• Straalsund, J. L., R. W. Powell, and B. A. Chin. 1982. An overview of neutron irradiation effects in LMFBR materials. Journal of Nuclear Materials 108 (305):109–299. doi: 10.1016/0022-3115(82)90499-8.
   Google Scholar
• Sun, C., F. A. Garner, L. Shao, X. Zhang, and S. A. Maloy. 2017. Influence of injected interstitials on the void swelling in two structural variants of 304L stainless steel induced by self-ion irradiation at 500 °C. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 409 (October):323–7. doi: 10.1016/j.nimb.2017.03.070.
   Google Scholar
• Wakai, E., N. Hashimoto, J. P Robertson, T. Sawai, and A. Hishinuma. 2002. Swelling of cold-worked austenitic stainless steels irradiated in HFIR under spectrally tailored conditions. Journal of Nuclear Materials 307–311:352–6. doi: 10.1016/S0022-3115(02)01189-3.
   Web of Science ®Google Scholar
• Watanabe, S. 2013. A widely applicable Bayesian information criterion. Journal of Machine Learning Research 14 (1):867–97.
   Google Scholar
• Yang, T-n., C. Lu, G. Velisa, K. Jin, P. Xiu, Y. Zhang, H. Bei, and L. Wang. 2019. Influence of irradiation temperature on void swelling in NiCoFeCrMn and NiCoFeCrPd. Scripta Materialia 158 (January):57–61. doi: 10.1016/j.scriptamat.2018.08.021.
   Google Scholar
• Zheng, S., K. Ristovski, A. Farahat, and C. Gupta. 2017. Long short-term memory network for remaining useful life estimation. In 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), 88–95. IEEE. doi: 10.1109/ICPHM.2017.7998311.
   Google Scholar
• Zhong, S., S. Fu, L. Lin, X. Fu, Z. Cui, and R. Wang. 2019. A novel unsupervised anomaly detection for gas turbine using isolation forest. In 2019 IEEE International Conference on Prognostics and Health Management (ICPHM), 1–6. IEEE. doi: 10.1109/ICPHM.2019.8819409.
   Google Scholar