Utility-Based Maintenance Optimization for Complex Water-Distribution Systems Using Bayesian Networks

Abstract

Water supply systems (WSS), as well as other real-world systems, are characterized by complex configurations. For these systems, it is essential to ensure appropriate utility through optimal maintenance planning. The difficulties in decision-making are much increased by lack of information regarding the operation and failure conditions. When maintenance optimization is considered for systems configured as networks, comprising a large number of components, the main challenge is to model the reliability characteristics, such as availability, taking account of the interactions and dependencies between different components. The aim of this paper is to provide an optimal Preventive Maintenance (PM) plan with a view to maximizing the utility of a complex repairable system using Bayesian Networks (BNs). For each node of the BN, the optimal PM periodicity is obtained, in accordance with the policy of periodic imperfect PM with minimal repair at failure. The system availability is then computed, by Bayesian inference, for various combinations of nodes, or subsystems, periodicities and partial renewals before the complete renewal of the whole system. A utility function is then introduced to provide the maintenance plan for the system, leading to the implementation of the best policy. The methodology is illustrated by numerical application on WSS.