Fault identification and analysis using artificial intelligence techniques for three-tank system

A fault can be defined as an unexpected change or malfunction in a system and a supervision system should be used to detect and identify the fault and its severity. A fault diagnosis system for online application must provide guaranteed response with its severity, so that a quick decision can be taken either for periodic maintenance if the severity of the fault is less or an immediate shut down if the severity is more. In this paper, a model-based fault diagnosis in a three-tank system has been done using artificial intelligence (AI). Two clogging faults are introduced at different locations with different magnitudes of severity. The A and B parameters of the state space model are estimated using single layer neural network and four AI techniques are used to detect the clogging fault along with severity. The results of different techniques of fault diagnosis are also presented and compared.