Sensor fault diagnosis in a chemical process via RBF neural networks

Radial basis function (RBF) neural networks are investigated here for process fault diagnosis. The use of the output prediction error, between a neural network model and a non-linear dynamic process, as a residual for diagnosing actuator, component and sensor faults is analysed. It is found that this residual for a dependent neural model is less sensitive to sensor faults than actuator or component faults. This is confirmed in experiments for a real, multivariable chemical reactor. A scheme is then developed utilising a semi-independent neural model to generate enhanced residuals for diagnosing the sensor faults in the reactor. A second neural-network classifier is developed to diagnose the sensor faults from the residuals generated, and results are presented to demonstrate the satisfactory detection and isolation of sensor faults achieved using this approach. (C) 1999 Elsevier Science Ltd. All rights reserved.