Title :
A fire detecting method based on multi-sensor data fusion
Author :
Chen, Shaohua ; Bao, Hong ; Zeng, Xianyun ; Yang, Yimin
Author_Institution :
Fac. of Autom., Guangdong Univ. of Technol., China
Abstract :
In this paper, a fire detection system is designed based on multi-sensor data fusion technology. According to the fire signal\´s inherent character, the around temperature, smoke density and CO density are considered as the chief fire detecting signals. By introducing the "feedback" of cybernetics, a new algorithm that extracts the fire data-fitting characteristic is presented. The algorithm may improve the accuracy and quickness of early warning with a tendency feedback signal. In this fire detecting system, the fire experiential characteristic and the fire data-fitting characteristic of fire signal data are fused by the fuzzy inference system to get the last fire probability. Finally simulation experiments are done for typical flaming fire, typical smoldering fire and the fire under typical disturbance signals in kitchen environment. And satisfactory results are obtained.
Keywords :
alarm systems; array signal processing; artificial intelligence; feedback; fires; fuzzy neural nets; safety; sensor fusion; CO density; artificial intelligence; feedback signal; fire data-fitting characteristic; fire detecting method; fire experiential characteristic; fire probability; fuzzy inference system; multisensor data fusion; neural network; smoke density; Cybernetics; Data mining; Feedback; Fires; Fuzzy systems; Gas detectors; Inference algorithms; Signal detection; Smoke detectors; Temperature;
Conference_Titel :
Systems, Man and Cybernetics, 2003. IEEE International Conference on
Print_ISBN :
0-7803-7952-7
DOI :
10.1109/ICSMC.2003.1244476