In many industrial environments, systems are required to perform a sequence of operations (or missions) with finite breaks between each operation. During these breaks, it may be advantageous to perform repair on some of the system’s components. However, it may be impossible to perform all desirable maintenance activities prior to the beginning of the next mission due to limitations on maintenance resources. In this paper, a mathematical programming framework is established for assisting decision‐makers in determining the optimal subset of maintenance activities to perform prior to beginning the next mission. This decision‐making process is referred to as selective maintenance. The selective maintenance models presented allow the decision‐maker to consider limitations on maintenance time and budget, as well as the reliability of the system. Selective maintenance is an open research area that is consistent with the modern industrial objective of performing more intelligent and efficient maintenance.

Abstract. The approach of using ontology reasoning to cleanse the output of information extraction tools was first articulated in SemantiClean. A limiting factor in applying this approach has been that ontology reasoning to find inconsistencies does not scale to the size of data produced by information extraction tools. In this paper, we describe techniques to scale inconsistency detection, and illustrate the use of our techniques to produce a consistent subset of a knowledge base with several thousand inconsistencies.

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