Sensor fault diagnosis in a chemical process via RBF neural networks

摘要

Radial basis function (RBF) neural networks are investigated here for process fault diagnosis. The sue 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 odel 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 he residuals generated, a nd results are presented to demonstrate the satisfactory detection and isolation of sensor faults achieved using this approach.