OPTIMAL HEURISTICS FOR RELIABILITY-BASED INSPECTION AND MAINTENANCE PLANNING

Inspection and maintenance planning of most engineering systems is based on prescriptive rules and ad-hoc planning. There is hence a significant potential for savings or improved performance by the application of smarter inspection and maintenance (I&M) planning. In general, I&M planning belongs to the class of sequential decision processes. Finding the theoretically optimal solution for such processes in realistic engineering systems is not possible at present, due to the complexity of these systems and the involved maintenance processes, which lead to intractably large state and policy spaces. Heuristics, which parametrize the policies, offer an alternative that is computationally tractable. If chosen well, these heuristics can lead to near-optimal I&M policies. In addition, they have the advantage of being easily interpretable, which is of importance in practical implementations. In this contribution, we look at two example systems, offshore steel structures and feeder pipes in nuclear power plants. We utilize physics-based stochastic models to describe the system performances and to assess the effect of inspections and maintenance on the system reliability. We discuss the formulation of possible heuristics for inspection and maintenance policies. On this basis, we calculate the benefit of using advanced reliability-based I&M planning over existing rule-based I&M planning in terms of the I&M costs and the resulting risks.