Abstract: Aiming at the false alarm defect prone to conventional monitoring devices during fire outbreaks, this paper proposes a fire detection algorithm combining fuzzy neural network with D-S evidence theory. Firstly, an intelligent fire detection algorithm is established by integrating the fuzzy neural network and D-S evidence theory. Secondly, fire characteristic parameters including temperature, smoke concentration and CO concentration are taken as the input variables of the proposed algorithm to realize reliable fire identification. Finally, a fire detection system based on the above algorithm is developed and verified via experiments under four typical fire conditions: polyurethane open flame, ethanol open flame, smoldering cotton rope fire and smoldering timber fire. Experimental results demonstrate that the detection accuracy of the developed fire detection system reaches 92.8%, 91.3%, 97.8% and 91.6% respectively for the four fire scenarios, delivering substantially superior performance compared with alternative contrast algorithms. In addition, the average fire response time of the developed system is 2.1 seconds shorter than that of commercially available fire detectors, with a false alarm rate limited to approximately 0.63%.

Key words: fire detection, fire characteristic parameter, fuzzy neural network, D-S evidence theory