Abstract
A neural network based fault diagnosis system is being developed for use in aerospace systems in which a family of neural nets replaces an online simulation process. A new neural network implementation of one of the model-based algorithms was developed. The authors summarize a series of computer experiments designed to benchmark the performance of this neural network based fault diagnosis algorithm in an environment where the good components of the systems are only known up to a tolerance band. The results indicate that neural networks can generalize around the data on which they were trained, yielding better performance for unforeseen inputs than traditional algorithms in the case of the fault diagnosis problem described. >
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