Modeling and analysis of a Scheduled Maintenance System: a DSPN approach

摘要

This paper describes a way of managing the modeling and analysis of Scheduled Maintenance Systems (SMSs) within an analytically tractable context. We chose a significant case study having a variety of interesting features like a heavily redundant architecture and a test and maintenance policy whose execution is made on-line without halting the system. We applied a methodology we previously developed based on the Deterministic Stochastic Petri Net (DSPN) approach, where the underlying stochastic process is Markov regenerative (MRGP) solved in our setting using an efficient analytical solution method. This methodology is implemented by the DEEM tool specifically developed for modeling and evaluating the dependability of Phased Mission Systems (PMSs). We test our methodology with such a case study to check whether it can master real and complex SMS problems and to compare its efficacy with traditional approaches (fault trees). The paper also investigates the problem of the optimal tuning of a maintenance program, giving a useful decision support tool for evaluating the system performance from the early design stage.