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

Authors: S. Srinivasan, P. Kanagasabapathy, N. Selvaganesan

Addresses: Department of Instrumentation Engineering, MIT Campus, Anna University, Chennai, Tamilnadu, India. ' Department of Instrumentation Engineering, MIT Campus, Anna University, Chennai, Tamilnadu, India. ' Department of Space, Indian Institute of Space Science and Technology, Government of India, ISRO Post, Thiruvananthapuram 695022, Kerala, India

Abstract: 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.

Keywords: automation; BPN; back propagation networks; fault diagnosis; fault identification; fuzzy expert systems; Kohonen network; RBFN; radial basis function networks; three-tank systems; neural networks; fuzzy logic; tank clogging; artificial intelligence.

DOI: 10.1504/IJAAC.2010.029841

International Journal of Automation and Control, 2010 Vol.4 No.1, pp.84 - 101

Published online: 02 Dec 2009 *

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