Keywords: reliability, maintenance optimization, inspections, grouping policy, probabilistic degradation, Markov decision process, multi-component systems.

Maintenance and reliability play a crucial role in engineering systems. Reliability-based maintenance optimization (RBMO) is an active research topic where the decision-maker has to manage various challenging objectives, such as cost, reliability, performance and availability. The maintenance policy is subject to considerable uncertainties related to degradation models, inspection outcomes, repair efficiency, monetary values, and component interdependence. When multi-component systems are considered [1], the complexity is greatly increased due to economic, stochastic and structural dependence.

The RBMO aims to define the best set of actions and times that should be considered to improve the multi-objective performance. When considering a single component, the maintenance policy can be defined in terms of preventive and corrective costs, with solutions depending on the considered facilities. When inspections and repair decisions are involved, the complexity of the problem increases as a result of the large number of possible paths to be considered over the planning horizon. In this case, the use of the partially observable Markov decision process [2] allows us to deal with complex sequence of decisions during each time period of the system lifetime.

In multi-component systems, the interdependence between components must be considered from the economic, stochastic and functional points of view. When the system is down, the opportunity to preventively replace non-failed components can be considered to improve the policy. The maintenance optimization of multiple systems or structures under budget constraints is a practical decision problem where the economic interdependence plays a fundamental role in defining the appropriate actions.

Many challenges have to be faced in RBMO regarding the elaboration of robust formulations for degradation, cost and processes, on the one hand, and the efficient solution procedures for complex systems, on the other hand. The solution procedure of RBMO problems is still a difficult task because of the combination of the large number of discrete and continuous variables, in addition to the qualitative information and decisions involved in the process. Although the use of probabilistic algorithms allows us to solve complex problems, the number of alternative decision paths becomes astronomic for real engineering systems. Despite substantial progress in maintenance optimization in recent decades, the existing methodologies still to be improved and adapted to real-world requirements regarding the increasing complexity, the growing size of systems and the rational use of financial and human resources.

[1]

R. Laggoune, A. Chateauneuf, D. Aissania, "Impact of few failure data on the opportunistic replacement policy for multi-component systems", Reliability Engineering and System Safety, 95(2), 108-119, 2010.

[2]

R.B. Corotis, J.H. Ellis, M. Jiang, "Modeling of risk-based inspection, maintenance and life-cycle cost with partially observable Markov decision process", Structure and Infrastructure Engineering, 1, 75-84, 2005.

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