Neural Based Parameter Identification and Fault Diagnosis in a Three-Tank System

The field of system identification is an important discipline of engineering which is widely used in control engineering practices and fault diagnosis. This paper describes a new methodology for parameter identification in a three-tank system through neural network and there by detecting a fault. The parameters are obtained by mapping between the process inputs and its states to the predicted states. The weights of the network represent the parameters of the system. The identified parameters are thus used to diagnose the clogging fault using Kohonen neural network.