Using Multivariate Statistical Methods to Detect Fires.
Using Multivariate Statistical Methods to Detect Fires.
(678 K)
McAvoy, T. J.; Milke, J. A.; Kunt, T. A.
Fire Technology, Vol. 32, No. 1, 6-24, January/February
1996.
Sponsor:
National Institute of Standards and Technology,
Gaithersburg, MD
Keywords:
fire detection systems; fire detectors; false alarms;
sensors; experiments; fire tests; detection time
Abstract:
Fire detectors must accurately detect fires, but they
should not respond to false alarms. Contemporary smoke
detectors sometimes cannot discriminate between smoke
and odor sources. These detectors can also be slow in
responding to smoldering fire sources. In this paper, a
statistical approach for detecting fires based on fusing
sensor signals from multiple sensors is presented. The
multivariate statistical approach, called principal
component analysis, is used to compress the sensor
information down to a small number of variables that can
be interpreted more easily than the raw sensor signals
themselves. Experimental results presented here show
that the proposed approach is more accurate than a
conventional smoke alarm, particularly for early
detection of smoldering fires. However, this new
approach does not overcome the problem of false alarms.
In spite of this current limitation, the method
discussed holds great promise for future fire detection.
Building and Fire Research Laboratory
National Institute of Standards and Technology
Gaithersburg, MD 20899