- Author
-
Jones, W. W.
- Title
- Development of a Multi-Criteria Algorithm for Fast and Reliable Fire Detection.
- Coporate
- National Institute of Standards and Technology, Gaithersburg, MD
- Book or Conf
- International Conference on Automatic Fire Detection "AUBE '04", 13th Proceedings. University of Duisburg. [Internationale Konferenz uber Automatischen Brandentdeckung.] September 14-16, 2004,
Duisburg, Germany,
Luck, H.; Laws, P.; Willms, I., Editors,
184-195 p.,
2004
- Keywords
-
fire detection
|
fire detection systems
|
algorithms
|
neural networkd
|
CFAST
|
sensors
- Identifiers
- CFAST (Consolidated Fire growth And Smoke Transport); curve matching algorithms; Artificial Neural Networks (ANN)
- Abstract
- The purpose of detecting fires early is to provide an alarm when there is an environment which is deemed to be a threat to people or a building. High reliability detection is based on the supposition that it is possible to utilize a sufficient number of sensors to ascertain unequivocally that there is a growing threat either to people or to a building and provide an estimation of the seriousness of the threat. It has been shown to be possible to detect fires early and reliably using the analog signal of the current generation of fire detectors. The best combination for early detection has been shown to be the complement of ionization, photoelectric, carbon monoxide and temperature. This is "best" in the sense that it is possible, using current day sensors, to see characteristic signatures very early, as well as to deduce quantitative information beyond the normal tenability limits. This paper will demonstrate this with an example using a neural network trained with a model of fire growth and smoke spread.