1 research outputs found
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