Neural networks for fault diagnosis and identification of industrial processes

In this work a model--based procedure exploiting analytical redundancy via state estimation techniques for the diagnosis of faults regarding sensors of a dynamic system is presented. Fault detection is based on Kalman filters designed in stochastic environment. Fault identification is therefore performed by means of different neural network architectures. In particular, neural networks are used as function approximators for estimating sensor fault sizes. The proposed fault diagnosis and identification tool is tested on a industrial gas turbine.