Recovery Block Reliability Analysis with Failure Clustering

New discrete- and continuous-time models of the recovery block are presented using Markov chain techniques. The time to failure distributions are fully described by their probability generating functions and Laplace transforms respectively. A discrete-time model is developed accounting for the commonly observed clustering of failure points in the input space. The Markov framework is a useful tool to solve this model even though the system’s behaviour is non-Markovian: additional states are artificially introduced to model the degraded state of individual modules.

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