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.

信息价值（VoI）原则可以指导对工程系统的最佳探索，该原则考虑了组件的拓扑重要性，可靠性和管理成本。对于串联系统，在大多数情况下，对不可靠的组件应给予更高的检查优先级。对于冗余系统（例如并行系统），单次决策问题的分析表明，应将更高的检查优先级给予更可靠的组件。本文研究了在进行顺序检查和修复的长期决策中对冗余系统的最佳探索。当考虑了预期的，累计的，打折的成本时，为可靠性较差的组件提供更高的检查优先级可能会更加有效，以保留系统冗余。为了调查这个问题，我们为冗余系统的顺序检查和维护开发了部分可观察的马尔可夫决策过程（POMDP）框架，在该框架中，VoI分析被嵌入到探索性行动的最佳选择中。我们调查了在并行和更通用的系统上使用替代近似POMDP求解器的情况，比较了它们的计算复杂性和性能，并显示了检查优先级如何取决于经济折扣率，降级率，检查精度和维修成本。