Citation

Derbel F. Fire Safety J. 2004; 39(5): 383-398.

Copyright

(Copyright © 2004, Elsevier Publishing)

DOI

unavailable

PMID

unavailable

Abstract

Nowadays, fire detection systems are used world wide in order to protect life and goods. However, at the present time, detectors show poor features with regard to detection speed and reliability. They respond to only a few fire parameters like smoke particles and they do not take into account other important fire parameters such as gaseous products. In this paper, we present a new multi-sensor detector consisting of a commercial optical fire detector, a temperature sensor and selected semiconductor metal oxide gas sensors. The use of a multi-sensor detector requires a more sophisticated algorithm than the simple threshold rule. The new algorithms are typically based on pattern recognition systems, consisting of a pre-processing unit, a feature extraction unit and a classification unit. The choice of suitable methods for the feature extraction and the classification is difficult. Most often, the classifier depends on the type of the extracted features. In this paper two methods for the feature extraction with their suitable classifiers are presented and compared. However the classification is based on neural networks.The first algorithm consists of (i) a pre-processing unit; and (ii) a FFT-based feature extraction unit to resolve characteristic fire signatures. For that purpose a moving window has been introduced and a composed signal has been generated from the different pre-processed sensor responses. The algorithm also consists of (iii) a classification unit with a Learning Vector Quantization (LVQ) neural network to classify the extracted features to fire, not fire, or disturbing event.The second algorithm consists of a pre-processing unit and feature extraction method based on the scaling of the quadratic mean value. For this kind of feature extraction a back-propagation neural network as a classifier has been chosen.An important improvement towards the use of commercial detectors has been achieved using both of the above-described algorithms. The neural networks with suitable feature extraction methods were able to detect test fires more quickly than the commercial optical fire detector without generating false alarms by disturbing events.