An Automated Failure Modes and Effect Analysis Based Visual Matrix Approach to Sensor Selection and Diagnosability Assessment

This paper builds on the ability to produce a comprehensive automated Failure Modes and Effects Analysis (FMEA) using qualitative model based reasoning techniques. The automated FMEA provides a comprehensive set of fault–effect relations by qualitative simulation and can be performed early in the design process. The comprehensive nature of the automated FMEA results in a fault-effect mapping that can be used to investigate the diagnosability of the system. A common requirement is to facilitate cost reductions by removing sensors or to improve diagnosability by including additional sensors. Measurements are typically expensive (in the broadest sense) and the problem addressed by this paper is how to allow select a set that fulfills the diagnosability requirements of the system. This paper documents a technique that provides an engineer with easy access to information about diagnostic capability via a matrix visualisation technique. The focus of the work was for 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.