We attempt to address the issues associated with reliability estimation for phased-mission systems (PMS) and present a novel datadriven\napproach to achieve reliability estimation for PMS using the condition monitoring information and degradation data of such\nsystem under dynamic operating scenario. In this sense, this paper differs from the existing methods only considering the static\nscenario without using the real-time information, which aims to estimate the reliability for a population but not for an individual. In\nthe presented approach, to establish a linkage between the historical data and real-time information of the individual PMS,we adopt\na stochastic filtering model to model the phase duration and obtain the updated estimation of the mission time by Bayesian law at\neach phase. At themeanwhile, the lifetime of PMS is estimated fromdegradation data, which are modeled by an adaptive Brownian\nmotion. As such, themission reliability can be real time obtained through the estimated distribution of the mission time in conjunction\nwith the estimated lifetime distribution.We demonstrate the usefulness of the developed approach via a numerical example.
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