Development of a Fire Detection System Using FT-IR Spectroscopy and Artificial Neural Networks.
Development of a Fire Detection System Using FT-IR
Spectroscopy and Artificial Neural Networks.
Chen, Y.; Serio, M. A.; Sathyamoorthy, S.
Fire Safety Science. Proceedings. Sixth (6th)
International Symposium. International Association for
Fire Safety Science (IAFSS). July 5-9, 1999, Poitiers,
France, Intl. Assoc. for Fire Safety Science, Boston,
MA, Curtat, M., Editor(s), 791-802 pp, 2000.
Sponsor:National Institute of Standards and Technology,
fire research; fire safety; fire science; fire detection
systems; FT-IR; spectroscopy; neural networks; fire
detection; false alarms; vapor phases; cables
Extensive measurements of flaming and smoldering fires
and nuisance/environmental sources were performed with
Fourier Transform Infrared (FT-IR) spectroscopy of gas
phase products. A neural network model was formulated
using the so-called Learning Vector Quantization (LVQ)
network approach. The LVQ approach contains input and
output layers with a hidden layer being a Kohenen layer.
The hidden layer learns and performs classification. The
inputs to the network are concentrations (from FT-IR
measurements) of eighteen (18) gas species. The outputs
of the network are classification of the input data as a
flaming fire, smoldering fire, nuisance or environmental
source. The network was trained and tested using the
test data collected during this project. The results
were very successful as, among the 248 cases tested,
only 12 cases were misclassified, mostly due to the
difficulties in classifying the modes of combustion
during a transition from a smoldering to a flaming tire.
Each case represents the gas phase concentration data at
a time step from one of the validation fires, which were
different types of fires from the training set. A first
generation fire detection system us-ing FT-IR gas
measurements and neural networks has been built and