Reliability modeling: combining self-healing characteristics and dynamic failure thresholds

References

• Ali, S. (2021). First passage time control charts assuming power law intensity for time to jointly monitor time and magnitude. Quality and Reliability Engineering International, 37(5), 2034–2064. https://doi.org/10.1002/qre.2844
   Web of Science ®Google Scholar
• Ali, S. (2022). Monitoring time and magnitude based on the renewal reward process with a random failure threshold. Journal of Applied Statistics, 48(2), 247–284. https://doi.org/10.1080/02664763.2020.1723502
   Web of Science ®Google Scholar
• Ali, S., & Pievatolo, A. (2018). Time and magnitude monitoring based on the renewal reward process. Reliability Engineering & System Safety, 179, 97–107. https://doi.org/10.1016/j.ress.2018.01.004
   Web of Science ®Google Scholar
• Allan, G., & Husler, J. (1999). Extreme shock models. Extremes, 2(3), 295–307. https://doi.org/10.1023/A:1009959004020
   Google Scholar
• Andreas, J., Dadgar, M., & Wietheger, W. (2019). Impact of the oil temperature on the frictional behavior of laser-structured surfaces. Tribology Transactions, 62(3), 443–451. https://doi.org/10.1080/10402004.2019.1571656
   Web of Science ®Google Scholar
• Ann Klutke, G., & Yang, Y. (2002). The availability of inspected systems subject to shocks and graceful degradation. IEEE Transactions on Reliability, 51(3), 371–374. https://doi.org/10.1109/TR.2002.802891
   Web of Science ®Google Scholar
• Blaiszik, B. J., Kramer, S. L., Olugebefola, S. C., Moore, J. S., Sottos, N. R., White, S. R., & Mlholt, T. E. (2010). Self-healing polymers and composites. Annual Review of Materials Research, 40(1), 179–211. https://doi.org/10.1146/annurev-matsci-070909-104532
   Google Scholar
• Chunyang, L., Chen, X., Xiaoshan, Y., Degraded systems with multiple performance parameters subject to shocks. in 2010 Proceedings - Annual Reliability and Maintainability Symposium (RAMS). 2010, https://doi.org/10.1109/RAMS.2010.5447968.
   Google Scholar
• Dai, X., Sheng, Q., Sui, H., & Pingbo, W. (2022). Reliability modelling of wheel wear deterioration using conditional bivariate gamma processes and Bayesian hierarchical models. Reliability Engineering & System Safety, 226, 108710. https://doi.org/10.1016/j.ress.2022.108710
   Web of Science ®Google Scholar
• Dong, W., Liu, S., Cao, Y., Ahmed Javed, S., & Du, Y. (2020). Reliability modeling and optimal random preventive maintenance policy for parallel systems with damage self-healing. Computers & Industrial Engineering, 142(106359), 106359. https://doi.org/10.1016/j.cie.2020.106359
   Google Scholar
• Dong, W., Liu, S., Hoo Bae, S., & Cao, Y. (2021). Reliability modelling for multi-component systems subject to stochastic deterioration and generalized cumulative shock damages. Reliability Engineering & System Safety, 205, 107260. https://doi.org/10.1016/j.ress.2020.107260
   Web of Science ®Google Scholar
• Gao, H., Cui, L., & Qiu, Q. (2019). Reliability modeling for degradation-shock dependence systems with multiple species of shocks. Reliability Engineering and System Safety, 185, 133–143. https://doi.org/10.1016/j.ress.2018.12.011
   Web of Science ®Google Scholar
• Georgia Ann, K., & Yoonjung, Y. (2002). The Availability of Inspected Systems Subject to Shocks and Graceful Degradation. IEEE Transactions on Reliability, 51(3), 371.
   Web of Science ®Google Scholar
• Gunnlaugsson, H. P., Bharuth-Ram, K., Johnston, K., Langouche, G., Mantovan, R., Mlholt, T. E., Naidoo, D., Ólafsson, O., & Weyer, G. (2015). Damage annealing in low temperature Fe/Mn implanted ZnO. Hyperfine Interactions, 230(1), 175–180. https://doi.org/10.1007/s10751-014-1096-6
   Google Scholar
• Gut, A. (1990). Cumulative shock models. Advances in Applied Probability, 22(2), 504–507. https://doi.org/10.2307/1427554
   Web of Science ®Google Scholar
• Hao, S., & Yang, J. (2018). Reliability analysis for dependent competing failure processes with changing degradation rate and hard failure threshold levels. Computers & Industrial Engineering, 118, 340–351. https://doi.org/10.1016/j.cie.2018.03.002
   Web of Science ®Google Scholar
• Hao, S., Yang, J., Xiaobing, M., & Zhao, Y. (2017). Reliability modeling for mutually dependent competing failure processes due to degradation and random shocks. Applied Mathematical Modelling, 51, 1232–1249. https://doi.org/10.1016/j.apm.2017.06.014
   Web of Science ®Google Scholar
• Helu, A., & Samawi, H. (2015). The Inverse weibull distribution as a failure model under various loss functions and based on progressive first-failure censored data. Quality Technology & Quantitative Management, 12(4), 517–535. https://doi.org/10.1080/16843703.2015.11673434
   Web of Science ®Google Scholar
• Jiang, L., Feng, Q., & Coit, D. W. (2012). Reliability and maintenance modeling for dependent competing failure processes with shifting failure thresholds. IEEE Transactions on Reliability, 61(4), 932–948. https://doi.org/10.1109/TR.2012.2221016
   Web of Science ®Google Scholar
• Jin, C., Ran, Y., Wang, Z., Huang, G., Xiao, L., & Zhang, G. (2020). Reliability analysis of gear rotation meta-action unit based on Weibull and inverse Gaussian competing failure process. Engineering Failure Analysis, 117, 104953. https://doi.org/10.1016/j.engfailanal.2020.104953
   Web of Science ®Google Scholar
• Kong, X., & Yang, J. (2020). Reliability analysis of composite insulators subject to multiple dependent competing failure processes with shock duration and shock damage self-recovery. Reliability Engineering & System Safety, 204, 107166. https://doi.org/10.1016/j.ress.2020.107166
   Web of Science ®Google Scholar
• Lim, H., Soo Kim, Y., Joo Bae, S., & Sung, S.I. (2017). Partial accelerated degradation test plans for Wiener degradation processes. Quality Technology & Quantitative Management, 16(1), 67–81. https://doi.org/10.1080/16843703.2017.1368968
   Web of Science ®Google Scholar
• Liu, H., Yeh, R.H., & Cai, B. (2017). Reliability modeling for dependent competing failure processes of damage self-healing systems. Computers & Industrial Engineering, 105, 55–62. https://doi.org/10.1016/j.cie.2016.12.035
   Web of Science ®Google Scholar
• Long, C. C. (1991). Competing risks in mortality analysis. Annual Review of Public Health, 12(1), 281–307. https://doi.org/10.1146/annurev.pu.12.050191.001433
   PubMedGoogle Scholar
• Parvardeh, A., & Balakrishnan, N. (2015). On mixed δ-shock models. Statistics & Probability Letters, 102, 51–60. https://doi.org/10.1016/j.spl.2015.04.006
   Web of Science ®Google Scholar
• Peng, H., Feng, Q., & Coit, D. W. (2011). Reliability and maintenance modeling for systems subject to multiple dependent competing failure processes. IIE Transactions, 43(1), 12–22. https://doi.org/10.1080/0740817X.2010.491502
   Web of Science ®Google Scholar
• Rafiee, K., Feng, Q., & Coit, D. W. (2017). Reliability assessment of competing risks with generalized mixed shock models. Reliability Engineering & System Safety, 159(3), 1–11. https://doi.org/10.1016/j.ress.2016.10.006
   Google Scholar
• Rafieem, K., Feng, Q., & Coit, D. W. (2014). Reliability modeling for dependent competing failure processes with changing degradation rate. IIE Transactions, 46(5), 283–496. https://doi.org/10.1080/0740817X.2013.812270
   Google Scholar
• Rafifiee, K., Feng, Q., & Coit, D. W. (2015). Condition-based maintenance for repairable deteriorating systems subject to a generalized mixed shock model. IEEE Transactions on Reliability, 64(4), 1164–1174. https://doi.org/10.1109/TR.2015.2461217
   Web of Science ®Google Scholar
• Shangguan, A., Xie, G., Fei, R., Lingxia, M., & Hei, X. (2022). Train wheel degradation generation and prediction based on the time series generation adversarial network. Reliability Engineering & System Safety, 229, 108816. https://doi.org/10.1016/j.ress.2022.108816
   Web of Science ®Google Scholar
• Tang, D., Jinsong, Y., Chen, X., & Makis, V. (2015). An optimal condition-based maintenance policy for a degrading system subject to the competing risks of soft and hard failure. Computers & Industrial Engineering, 83, 100–110. https://doi.org/10.1016/j.cie.2015.02.003
   Web of Science ®Google Scholar
• Tanner, D. M., Dugger, M. T. (2003). Wear mechanisms in a reliability methodology. Proceedings of SPIE - The International Society for Optical Engineering, San Jose, CA, (Vol. 4980, pp. 22–40).
   Google Scholar
• Wang, J., Zhigang Li, Bai, G., Zuo, M. J., & Li, M. J. (2020). An improved model for dependent competing risks considering continuous degradation and random shocks. Reliability Engineering & System Safety, 193, 106641. https://doi.org/10.1016/j.ress.2019.106641
   Web of Science ®Google Scholar
• Wang, X., Lin, L., Chang, M., & Han, K. (2021). Reliability modeling for competing failure process with shifting failure thresholds under severe product working conditions. Applied Mathematical Modelling, 89, 1747–1763. https://doi.org/10.1016/j.apm.2020.08.032
   Web of Science ®Google Scholar
• Wang, L., Yang, Y., Zhu, H., & Liu, G. (2019). Optimal condition-based renewable warranty policy for products with three-stage failure process. Quality Technology & Quantitative Management, 17(2), 216–233. https://doi.org/10.1080/16843703.2019.1584956
   Web of Science ®Google Scholar
• Wang, X., Zhao, X., & Sun, J. (2019). A compound negative binomial distribution with mutative termination conditions based on a change point. Journal of Computational and Applied Mathematics, 351, 237–249. https://doi.org/10.1016/j.cam.2018.11.009
   Web of Science ®Google Scholar
• Wei, H., & Askin, R. G. (2003). Reliability analysis of electronic devices with multiple competing failure modes involving performance aging degradation. Quality and Reliability Engineering International, 19(3), 241–254. https://doi.org/10.1002/qre.524
   Web of Science ®Google Scholar
• Yousefi, N., Coit, D. W., & Zhu, X. (2020). Dynamic maintenance policy for systems with repairable components subject to mutually dependent competing failure processes. Computers & Industrial Engineering, 143, 106398. https://doi.org/10.1016/j.cie.2020.106398
   Web of Science ®Google Scholar
• Zehui, L., & Kong, X. (2007). Life behavior of δ-shock model. Statistics & Probability Letters, 77(6), 577–587. https://doi.org/10.1016/j.spl.2006.08.008
   Web of Science ®Google Scholar
• Zhao, X., Guo, X., & Wang, X. (2018). Reliability and maintenance policies for a two-stage shock model with self-healing mechanism. Reliability Engineering and System Safety, 172, 185–194. https://doi.org/10.1016/j.ress.2017.12.013
   Web of Science ®Google Scholar
• Zhixin, X., Yang, J., Zhang, T., Nuli, Y., Wang, J., & Hiranom, S.I. (2018). Silicon microparticle anodes with self-healing multiple network binder. Joule, 2(5), 950–961. https://doi.org/10.1016/j.joule.2018.02.012
   Web of Science ®Google Scholar