Abstract
Abstract This paper investigates some aspects of the application of neural networks and genetic algorithms to fault diagnosis in dynamic systems. It refers to a fault diagnosis scheme based on parameter estimation; fault detection and identification are carried out by means of suitable classifiers. Three kinds of classifiers are considered, which use the parameters estimated by means of a parameter estimator; one is a neural network with a vectorial output; another is a neural network with a scalar output; the last one is developed by means of the application of genetic algorithms and consists of a scalar function. A simulation example is reported; the genetic classifier is simpler than the neural ones and shows a certain robustness with respect to input noise.
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