Fault diagnosis and neural networks

The use of neural networks to implement a model-based fault diagnosis algorithm is discussed. The method resolves the fundamental computational complexity problem which has historically limited the applicability of model-based techniques. This is achieved by using the neural network to implement the equation solver associated with these techniques. The neural network implementation paves the way for real-time operation by transforming the online computation usually associated with model-based fault diagnosis techniques into an offline training process while simultaneously reducing the sensitivity of the algorithm to tolerance effects. >