This paper builds on the ability to produce a comprehensive automated Failure Modes and Effects Analysis using qualitative model based reasoning techniques. From the FMEA output a diagnostic system comprised of a set of symptoms and associated potential faults can be generated and used as the basis of an on-board or off-board diagnostic system. This makes it is easy to propose additional sensing possibilities for the system, however a method is required to allow an appropriate set of sensors to be selected that provide the required level of diagnosability. The large number of competing factors outside of the scope of the modelling combined with the additional system knowledge required makes it difficult to optimise automatically. This paper therefore documents a semi automated technique that provides an engineer with easy access to information about diagnostic capability via a matrix visualisation technique. The focus of the project was the fuel system of an Uninhabited Aerial Vehicle(UAV) although the system has also been used on an automotive electrical system, and is applicable to a wide range of schematic and component based systems.
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