Application of Neural Networks for Discriminating Fire Detectors.
Application of Neural Networks for Discriminating Fire
Detectors.
(438 K)
Milke, J. A.
University of Duisburg. International Conference on
Automatic Fire Detection "AUBE '95", 10th. April 4-6,
1995, Duisburg, Germany, Luck, H., Editor(s), 213-222
pp, 1995.
Sponsor:
National Institute of Standards and Technology,
Gaithersburg, MD
Keywords:
fire detection; fire detectors; experiments; small scale
fire tests; large scale fire tests; smoke; odors; expert
systems; smoldering; neural networks; light obscuration
Abstract:
Research is being conducted to describe the
characteristics of an improved fire detector which
promptly reacts to smoke while discriminating between
smoke and odors from fire and non-fire sources. This
study is investigating signature patterns associated
with fire and environmental sources via small- and
large-scale tests toward the development of an improved
fire detector. On the tests, smoke and odors are
produced from a variety of conditions: flaming,
pyrolyzing and heated samples, and nuisance sources,
such as aerosols, household products and cooked food.
Measurements include light obscuration, temperature,
mass loss, CO, CO2, O2 and oxidizable gas
concentrations. The feasibility of an elementary expert
system to classify the source of the signatures from
small-scale experiments was demonstrated in the first
phase. In the recently completed second phase, a
similar expert system correctly classified the source of
the signatures in large-scale experiments in 85% of the
cases. Neural networks have been applied to both sets of
data from the small- and large-scale tests providing an
even greater successful classification rate.
