EARLY WARNING FIRE DETECTION SYSTEM USING AN ELECTRONIC NOSE

摘要

The Navy program Damage Control-Automation for Reduced Manning is focused on enhancing automation of ship functions and damage control systems. A key element to this objective is the improvement of current fire detection systems. Improved reliability is needed such that fire detection systems can automatically control fire suppression systems. The use of multi-criteria based detection technology offers the most promising means to achieve both improved sensitivity to real fires and reduced susceptibility to nuisance alarm sources. A multi-criteria early warning fire detection system consisting of four sensors for early fire detection and nuisance source rejection was developed using a Probabilistic Neural Network. The prototype early warning fire detector was built and tested in a shipboard environment. The prototype system provides reliable warning of actual fire conditions in less time with fewer nuisance alarms than can be achieved with commercially available smoke detection systems.