The primary objective of this research is the development and demonstration of a global Failure Detection and Identification (FDI) Design Assistant (DA) prototype based on expert system technology. A global FDI algorithm performs its detection, isolation and estimation function by assessing the global effects of a hardware fault and surface damage on the closed-loop aircraft dynamics. Since changes in the flight control law affect the signature of a hardware fault and surface damage, the design of a global FDI algorithm is dependent on the flight control law performance characteristics. Ideally, the flight control law and global FDI designs should be accomplished simultaneously, especially for a new aircraft design rather than the current practice of serial iterations on the two designs. Hence, automation of the global FDI design process is desirable in order to reduce development time, cost and risk for advanced flight control systems. A secondary study objective is to investigate how expert systems and neural networks can be integrated into the implementation of a global FDI system. We describe the implementation of the detection and isolation logic portions of the CRCA global FDI algorithm as a rule-based expert system so that symbolic knowledge representation and reasoning can be incorporated into the design.


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