Dynamic Reliability Assessment for Multi-State Systems Utilizing System-Level Inspection Data


Ancient time-based mostly reliability assessment ways evaluate the reliability of a multi-state system (MSS) from a population or a statistical perspective that the reliability of a system is computed purely based upon historical time-to-failure knowledge collected from a massive population of identical elements or systems. These ways, but, fail to characterize the stochastic behaviors of a specific individual system. During this paper, by utilizing system-level observation history, a dynamic reliability assessment method for MSSs is put forth. The proposed recursive Bayesian formula is able to dynamically update the reliability operate of a selected MSS over time by incorporating system-level inspection knowledge. The dynamic reliability perform, state probabilities, and remaining useful life distribution of an MSS in residual lifetime are derived for two common cases: the degradation of components follows a homogeneous continuous time Markov method, and a non-homogeneous continuous time Markov process. The effectiveness and accuracy of the proposed technique are demonstrated via two numerical examples.

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