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Neural Networks for Smart Fire Detection. Final Report.

pdf icon Neural Networks for Smart Fire Detection. Final Report. (7051 K)
Milke, J. A.; McAvoy, T. J.

NIST GCR 96-699; 317 p. December 1996.


National Institute of Standards and Technology, Gaithersburg, MD

Available from:

National Technical Information Service
Order number: PB97-138267


fire detection; experiments; data analysis; light obscuration


Research was conducted using multiple sensors with an algorithm to detect fires more quickly than currently available smoke detectors while also decreasing the susceptibility to unnecessary alarms. The effort involved the production of signatures from three types of sources: flaming fires, non-flaming fires and non-fire, nuisance sources, followed by analysis to recognize signature patterns for the three types of sources. The first phase of research consisted of establishing the feasibility of distinguishing between signatures from fire and non-fire sources using a small-scale apparatus. The second phase consisted of introducing the signatures in a 12 ft. square room with a height of 8 ft. Measurements included CO, CO2, and O2 concentrations, presence of oxidizable gases, light obscuration and temperature. The signatures measured could be associated with the three types of sources. Using a multivariate statistical analysis, the response time of a prototype detector was appreciably less than that of commercially available detectors, with a significant reduction in unnecessary alarm susceptibility. In the third phase, pairs of sources were provided simultaneously to determine if a nuisance source could mask the signature from a fire source and if two nuisance sources provide a signature similar to that from a fire. Results indicate that the ratio of the CO to CO2 concentrations is representative of flaming fire sources and to a limited extent for non-flaming fire sources, independent of the presence of a nuisance source.