A knowledge base system approach to inspection scheduling for fixed offshore platforms

In the offshore oil and gas industry in the UK, one of the most common forms of structure is the fixed steel jacket type of offshore platform. These are highly redundant structures subject to many random or uncertain factors. In particular, they are subject to uncertainties in the load distribution through the components, and to time-varying and cyclic loads leading to deterioration through fatigue. Operators are required to ensure the integrity of these structures by carrying out periodic inspections and repairing when necessary. Decisions on inspection, repair and maintenance (IRM) actions on structures involves making use of various tools and can be a complex problem. Traditionally, engineering judgement is employed to schedule inspections and deterministic analyses are used to confirm decisions. The use of structural reliability methods may lead to more rational scheduling of IRM actions. Applying structural reliability analysis to the production of rational inspection strategies, however, requires understanding the inspection procedure and making use of the appropriate information on inspection techniques. There are difficulties in collecting input data and the interpreted results need to be combined to form a rational global solution for the structure which takes into account practical constraints. The development of a knowledge base system (KBS) for reliability based inspection scheduling (RISC) provides a way of making use of complex quantitative objective analyses for scheduling. This thesis describes the development of a demonstrator RISC KBS. The general problems of knowledge representation and scheduling are discussed and schemes from Artificial Intelligence are proposed. Additionally, a system for automated inspection is described and its role in IRM of platforms is considered. A RISC System integrating suitable databases with fatigue fracture mechanics based reliability analysis within a KBS framework will enable operators to develop rational IRM scheduling strategies