A neural network approach to instrument fault detection and isolation

The growing diffusion of Artificial Neural Network (ANN) applications suggests the authors a possible solution to Instrument Fault Detection and Isolation (IFDI) problems. It is based on the modelling of both the measurement station and the system under analysis by a suitable ANN, having the input layer fed by instrument outputs and the output layer which gives information for faulty instrument detection and isolation. The methodologies adopted are described in detail and tested on a complex automatic measurement station for induction motor testing. The performance of the proposed IFDI scheme is experimentally evaluated mainly in terms of correct diagnosis, incorrect fault isolation, missed fault detection, and false alarm. The proposed diagnostic scheme proves to have good performance also out of the domain on which it was trained.<>