Modelling and numerical assessment of a maintenance strategy with stock through piecewise deterministic Markov processes and quasi Monte Carlo methods

Abstract

To ensure a power generation level, the French national electricity supply (EDF) has to manage its producing assets by putting in place adapted preventive maintenance strategies. In this paper, a fleet of identical components is considered, which are spread out all around France (one per power plant site). The components are assumed to have stochastically independent lifetimes but they are made functionally dependent through the sharing of a common stock of spare parts. When available, these spare parts are used for both corrective and preventive replacements, with priority to corrective replacements. When the stock is empty, replacements are delayed until the arrival of new spare parts. These spare parts are expensive and their manufacturing time is long, which makes it necessary to rigorously define their ordering process. The point of the paper is to provide the decision maker with the tools to take the right decision (make or not the overhaul). To do that, two indicators are proposed, which are based on an economic variable called the Net Present Value (NPV). The NPV stands for the difference between the cumulated discounted cash-flows of the purely corrective policy and the one including the overhaul. Piecewise Deterministic Markov Processes (PDMPs) are first considered for the joint modelling of the stochastic evolution of the components, stock and ordering process with and without overhaul. The indicators are next expressed with respect to these PDMPs, which have to be numerically assessed. Instead of using the most classical Monte Carlo (MC) simulations, we here suggest alternate methods based on quasi Monte Carlo simulations, which replace the random uniform numbers of the MC method by deterministic sequences called Low Discrepancy Sequences. The obtained results show a real gain of the quasi Monte Carlo methods in comparison with the MC method. The developed tools can hence help the decision maker to take the right decision.

Piecewise deterministic Markov process for condition-based imperfect maintenance models

“…In recent years, PDMPs achieve great attention in maintenance fields, such as [15,16,17,18,19,20]. Boukas et al [15] consider a maintenance and production model of a flexible manufacturing system using controlled PDMP, and derive the Chapman-Kolmogorov (C-K) equation of the system.…”

“…Zhang et al [16] deal with the offshore oil production using PDMP combined with the simulation method of Monte Carlo. Demagne et al [18] 1 INTRODUCTION consider a maintenance strategy with stock through PDMP and simulate the system with quasi Monte Carlo method. Lin et al [19] develop a maintenance model with cognitive uncertainty and damage interdependence using PDMP.…”

Industrial gas turbine operating parameters monitoring and data-driven prediction

“…The results of such model application may allow to assess different gas turbine systems reliability. Non-traditional reliability monitoring methods and novel approaches to reliability monitoring include vague lambda-tau methodology [23], fuzzy sets [37] and numerical assessment based on Piecewise Deterministic Markov Process and Quasi Monte Carlo methods [10]. The Petri Net model fed by Failure Tree Analysis (FTA) has also been presented and discussed by Verma and Kumar [35].…”