Neural observer schemes for robust detection and isolation of process faults

The present paper suggests neural approaches to observer-based schemes, in order to perform a robust diagnosis of process faults. The symptoms are generated by using dynamic neural networks with mixed structure. The residuals are then classified by means of static artificial nets. Application to a laboratory process is included. It refers to component and instrument fault detection and isolation in a three-tank system