Prototype Early Warning Fire Detection System: Test Series 2 Results
Abstract
The system under development combines a multi-criteria (sensor array) approach with sophisticated data analysis methods. Together an array of sensors and a multivariate classification algorithm has the potential to produce an early warning fire detection system with a low nuisance alarm rate. Several sensors measuring different parameters of the environment produce a pattern or response fingerprint for an event. Multivariate data analysis methods can be trained to recognize the pattern of an important event such as a fire. Multivariate classification methods, such as neural networks, rely on the comparison of events (i.e., fires) with non- events (i.e., background and nuisance sources). Variations in the response of sensors can be used to train an algorithm to recognize events when they occur. A key to the success of these methods is the appropriate design of sensor arrays and training sets of data used to develop the algorithm. This test series included a variety of conditions that may be encountered in a real shipboard environment. Every effort was made to consider many representative fire situations and potential interference sources. including the use of Navy approved materials.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 23, 2000
- Accession Number
- ADA383972
Entities
People
- Daniel T. Gottuk
- Hung Pham
- Jennifer T. Wong
- Mark T. Wright
- Susan L. Rose-Pehrsson
Organizations
- United States Naval Research Laboratory