Neural networks for the identification of gas cylinder faults
Date Issued
2007
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.
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.
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