Hybrid Stochastic Models for Remaining Lifetime Prognosis Dissertation

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The dissertation focuses on creating analytical models to estimate the remaining lifetime probability distribution of components in fluctuating environments. It explores three stochastic process models: temporally nonhomogeneous Markov, temporally homogeneous Markov, and semi-Markov environments. By integrating real-time degradation data from sensors with these models, it computes lifetime distributions, revealing that certain models yield matrix-exponential type distributions. To address computational challenges in the semi-Markov case, phase-type approximations are employed. The findings demonstrate the potential of these techniques for lifetime prognosis across various applications.