PROJECT TITLE :

Reliability Evaluation of Large-Scale Systems With Identical Units

ABSTRACT:

The reliability assessment of a giant-scale system that considers its units' degradation is challenging thanks to the resulting dimensionality drawback. We tend to propose a brand new methodology that enables us to beat difficulties in analyzing large-scale system dynamics, and devise analytical ways for locating the multivariate distribution of the dynamically changing system condition. When each unit's degradation condition can be classified into a finite number of states, and the transition distribution from one state to a different is understood, we obtain the asymptotic distribution of the quantity of units at each degradation state using fluid and diffusion limits. Specifically, we tend to use a uniform acceleration technique, and get the time-varying mean vector and therefore the covariance matrix of the amount of units at multiple degradation states. When a state transition follows a non-Markovian deterioration process, we tend to integrate part-kind distribution approximations with the fluid and diffusion limits. We show that, with any transition time distributions, the distribution of the quantity of units at multiple degradation conditions can be approximated by the multivariate Gaussian distribution as the overall range of units gets giant. The analytical results enable us to perform probabilistic assessment of the system condition throughout the system's service life. Our numerical studies counsel that the proposed ways can accurately characterize the stochastic evolution of the system condition over time.


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