Learning approach to fault tolerant control: an overview

Presents an overview of a learning methodology for detecting, identifying and accommodating faults in nonlinear dynamic systems. The main idea behind this approach is to monitor the plant for any off-nominal behavior due to faults utilizing a neural network or other types of online approximators. In the presence of a failure, the neural network can be used as an estimate of the nonlinear fault function for fault identification and accommodation purposes. Furthermore, during the initial stage of monitoring, the learning capabilities of the neural network can be used to learn the modeling errors, thereby enhancing the robustness properties of the fault diagnosis scheme.