An algorithmic approach for the dynamic reliability analysis of non-repairable multi-state weighted k-out-of-n:G system

• A multi-state weighted k-out-of-n:G system is studied.
• A Monte-Carlo simulation algorithm is provided for the dynamic analysis.
• Numerics are presented when the components׳ degradation follow the Markov process.

In this paper, we study a multi-state weighted k-out-of-n:G system model in a dynamic setup. In particular, we study the random time spent by the system with a minimum performance level of k. Our method is based on ordering the lifetimes of the system׳s components in different state subsets. Using this ordering along with the Monte-Carlo simulation algorithm, we obtain estimates of the mean and survival function of the time spent by the system in state k or above. We present illustrative computational results when the degradation in the components follows a Markov process.