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  1. null (Ed.)
    Optimal exploration of engineering systems can be guided by the principle of Value of Information (VoI), which accounts for the topological important of components, their reliability and the management costs. For series systems, in most cases higher inspection priority should be given to unreliable components. For redundant systems such as parallel systems, analysis of one-shot decision problems shows that higher inspection priority should be given to more reliable components. This paper investigates the optimal exploration of redundant systems in long-term decision making with sequential inspection and repairing. When the expected, cumulated, discounted cost is considered, it may become more efficient to give higher inspection priority to less reliable components, in order to preserve system redundancy. To investigate this problem, we develop a Partially Observable Markov Decision Process (POMDP) framework for sequential inspection and maintenance of redundant systems, where the VoI analysis is embedded in the optimal selection of exploratory actions. We investigate the use of alternative approximate POMDP solvers for parallel and more general systems, compare their computation complexities and performance, and show how the inspection priorities depend on the economic discount factor, the degradation rate, the inspection precision, and the repair cost. 
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  2. null (Ed.)
    We develop computable metrics to assign priorities for information collection on binary systems composed of binary components. Components are worth inspecting because their condition states are uncertain, and system functioning depends on them. The Value of Information (VoI) enables assessment of the impact of information in decision making under uncertainty, including the component’s reliability and role in the system, the precision of the observation, the available maintenance actions and the expected economic loss. We introduce the VoI-based metrics for system-level (“global”) and component-level (“local”) maintenance actions, analyze the properties of these metrics, and apply them to series and parallel systems. We discuss their computational complexity in applications to general network systems and, to tame the complexity for the local metric assessment, we present a heuristic and assess its performance on some case studies. 
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  3. Many infrastructure systems can be modeled as networks of components with binary states (intact, damaged). Information about components’ conditions is crucial for the maintenance process of the system. However, it is often impossible to collect information of all components due to budget constraints. Several metrics have been developed to assess the importance of the components in relation to maintenance actions: an important component is one that should receive high maintenance priority. Instead, in this paper we focus on the priority to be assigned for component inspections and information collection. We investigate metrics based on system level (global) and component level (local) decision making after inspection for networks with different topology, and compare these results with traditional ones. We then discuss the computational challenges of these metrics and provide possible approximation approaches. 
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