- Author
-
McAvoy, T. J.
|
Milke, J. A.
|
Kunt, T. A.
- Title
- Using Multivariate Statistical Methods to Detect Fires.
- Coporate
- Maryland Univ., College Park
- Journal
-
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
- Identifiers
- Principal Component Analysis (PCA); Partial Least Squares (PLS); multivariate statistical methods; PCA mathematics; modern sensor technology; applying PCA to fire experiments; detection times for conventional smoke alarm
- 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.