Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/863
Title: Neural networks for the identification of gas cylinder faults
Authors: Christodoulou, Constantinos 
Demosthenous, Demos
Cleanthous, Aris
Neocleous, Costas 
Keywords: Feedforward neural networks;Kohonen network;Gas cylinder
Issue Date: 2007
Source: proceedings of the 25th IASTED International Multi Conference on Artificial Intelligence and Applications, February 12-14, 2007, Innsbruck, Austria
Abstract: The safety of gas cylinders in domestic applications is of utmost importance. A crucial stage in the appraisal of the risk for failures and possible faults is occurring during the filling process. In Cyprus, this task is currently done by specialized workers who monitor the cylinders during filling. In order to explore the possibility for an automated risk appraisal and consequent screening, a system of fault identification using vibrational time series and neural network classification has been used. Two systems have been attempted. One using a multi-slab feedforward neural structure employing backpropagation-type learning, and a Kohonen self-organizing map. The results were also compared with different simple statistical methods. The feedforward net, proved to be slightly better responding than the Kohonen map for this particular problem.
URI: http://ktisis.cut.ac.cy/handle/10488/863
ISBN: 9780889866264
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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