Process fault diagnosis approach based on neural observers

This paper presents an approach to process fault diagnosis (FDI) in nonlinear dynamical systems, based on a bank of neural observers. Each neural observer is tuned to a particular fault and predicts, using its embedded model, the expected values for the sensor readings. The residuals, the difference between the sensor readings and the predicted readings, are used as fault indicators. Each neural observer is based on a multi-layer perceptron feed-forward neural network with external feedback connections, and an adjustable gain. The focus of this work is on diagnosis of parametric faults on process components, by means of analyzing the residuals. The proposed FDI technique has been implemented on a simulation model of a DC motor under closed-loop control. Results from experiments are presented and discussed.