Reliability assessment and lifetime prediction of degradation processes considering recoverable shock damages

References

• Bae, S.J. and Kvam, P.H. (2004) A nonlinear random coefficients model for degradation testing. Technometrics, 46(4), 460–469.
   Web of Science ®Google Scholar
• Bian, L. and Gebraeel, N. (2013) Stochastic methodology for prognostics under continuously varying environmental profiles. Statistical Analysis and Data Mining, 6(3), 260–270.
   Web of Science ®Google Scholar
• Csörgő, M. and Csörgő, S. (1987) Estimation of percentile residual life. Operations Research, 35(4), 598–606.
   Web of Science ®Google Scholar
• Cui, L., Chen, Z. and Gao, H. (2018) Reliability for systems with self-healing effect under shock models. Quality Technology and Quantitative Management, 15(5), 551–567.
   Web of Science ®Google Scholar
• Daniels, H.E. (1996) Approximating the first crossing-time density for a curved boundary. Bernoulli, 2(2), 133–143.
   Google Scholar
• Gebraeel, N. and Pan, J. (2008) Prognostic degradation models for computing and updating residual life distributions in a time-varying environment. IEEE Transactions on Reliability, 57(4), 539–550.
   Web of Science ®Google Scholar
• Guo, C., Wang, W., Guo, B. and Si, X. (2013) A maintenance optimization model for mission-oriented systems based on Wiener degradation. Reliability Engineering & System Safety, 111, 183–194.
   Web of Science ®Google Scholar
• Gut, A. (1990) Cumulative shock models. Advances in Applied Probability, 22(2), 504–507.
   Web of Science ®Google Scholar
• Gut, A. (2001) Mixed shock models. Bernoulli, 7(3), 541–555.
   Web of Science ®Google Scholar
• Gut, A. and Hüsler, J. (1999) Extreme shock models. Extremes, 2(3), 295–307.
   Google Scholar
• Hao, S., Yang, J., Ma, X. and Zhao, Y. (2017) Reliability modeling for mutually dependent competing failure processes due to degradation and random shocks. Applied Mathematical Modelling, 51, 232–249.
   Web of Science ®Google Scholar
• Huang, T., Peng, B., Coit, D.W. and Yu, Z. (2019) Degradation modeling and lifetime prediction considering effective shocks in a dynamic environment. IEEE Transactions on Reliability, 68(3), 819–830.
   Web of Science ®Google Scholar
• Jiang, L., Feng, Q. and 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.
   Web of Science ®Google Scholar
• Lawless, J. and Crowder, M. (2004) Covariates and random effects in a Gamma process model with application to degradation and failure. Lifetime Data Analysis, 10(3), 213–227.
   PubMed Web of Science ®Google Scholar
• Li, Z., Cui, L. and Chen, J. (2018) Traffic accident modelling via self-exciting point processes. Reliability Engineering & System Safety, 180, 312–320.
   Web of Science ®Google Scholar
• Lu, C.J. and Meeker, W.O. (1993) Using degradation measures to estimate a time-to-failure distribution. Technometrics, 35(2), 161–174.
   Web of Science ®Google Scholar
• Mallor, F. and Omey, E. (2001) Shocks, runs and random sums. Journal of Applied Probability, 38(2), 438–448.
   Web of Science ®Google Scholar
• Nakagawa, T. (2007) Shock and Damage Models in Reliability Theory, Springer-Verlag London Limited.
   Google Scholar
• Rafiee, K., Feng, Q. and Coit, D.W. (2014) Reliability modeling for dependent competing failure processes with changing degradation rate. IIE Transactions, 46(5), 483–496.
   Web of Science ®Google Scholar
• Rafiee, K., Feng, Q. and Coit, D.W. (2017) Reliability assessment of competing risks with generalized mixed shock models. Reliability Engineering & System Safety, 159, 1–11.
   Web of Science ®Google Scholar
• Shen, J., Cui, L. and Yi, H. (2018) System performance of damage self-healing systems under random shocks by using discrete state method. Computers and Industrial Engineering, 125, 124–134.
   Web of Science ®Google Scholar
• Shu, Y., Feng, Q., Kao, E.P.C. and Liu, H. (2016) Lévy-driven non-Gaussian Ornstein-Uhlenbeck processes for degradation-based reliability analysis. IIE Transactions, 48(11), 993–1003.
   Web of Science ®Google Scholar
• Si, X.-S., Wang, W., Hu, C.-H., Chen, M.-Y. and Zhou, D.-H. (2013) A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation. Mechanical Systems and Signal Processing, 35(1-2), 219–237.
   Web of Science ®Google Scholar
• Takács, L. (1960) Stochastic Processes, Wiley, New York, NY.
   Google Scholar
• Wang, G.-J. and Peng, R. (2017) A generalised δ-shock model with two types of shocks. International Journal of Systems Science: Operations & Logistics, 4(4), 372–383.
   Google Scholar
• Wang, X. (2010) Wiener processes with random effects for degradation data. Journal of Multivariate Analysis, 101(2), 340–351.
   Web of Science ®Google Scholar
• Wang, X., Balakrishnan, N. and Guo, B. (2014) Residual life estimation based on a generalized Wiener degradation process. Reliability Engineering & System Safety, 124, 13–23.
   Web of Science ®Google Scholar
• Whitmore, G.A. (1995) Estimating degradation by a Wiener diffusion process subject to measurement error. Lifetime Data Analysis, 1(3), 307–319.
   PubMedGoogle Scholar
• Xu, Z., Hong, Y. and Jin, R. (2016) Nonlinear general path models for degradation data with dynamic covariates. Applied Stochastic Models in Business and Industry, 32(2), 153–167.
   Web of Science ®Google Scholar
• Ye, Z.-S., Tang, L.-C. and Xu, H.-Y. (2011) A distribution-based systems reliability model under extreme shocks and natural degradation. IEEE Transactions on Reliability, 60(1), 246–256.
   Web of Science ®Google Scholar
• Zhai, Q. and Ye, Z.-S. (2018) Degradation in common dynamic environments. Technometrics, 60(4), 461–471.
   Web of Science ®Google Scholar
• Zuo, M.-J., Jiang, R. and Yam, R.C.M. (1999) Approaches for reliability modeling of continuous-state devices. IEEE Transactions on Reliability, 48(1), 9–18.
   Web of Science ®Google Scholar