Title :
Application of Self-Adaptive Neural Fuzzy Network in Early Detection of Conveyor Belt Fire
Author :
Guo Jian ; Zhu Jie ; Zhao Mingru ; Sun Yuan
Author_Institution :
Inf. Sch., Beijing WUZI Univ., Beijing, China
Abstract :
With the more usage of conveyor belts in coalmines, the possibility of fire is increasing and the loss caused by conveyor belts fire is becoming more and more. The traditional alarm system using single sensor or relatively simple algorithm processing fire signals cannot meet the need of belt fire detection in coal mine. Multi-sensor fire detection system is one of the important current developments in automatic fire detection technology. The crucial point in the detection is using more sophisticated algorithm to process fire signals. In this thesis, we introduce the principle of an algorithm based self-adaptive neural fuzzy network which has four inputs - temperature (T), rate of temperature change (¿T), dense of carbon monoxide (CO) and rate of CO dense change (¿CO). The experiment result shows that using this system can enhance the adaptive ability to environment and reinforce the ability of resisting interfere. At the same time, combining the fuzzy inference and neural network used in processing the fire signals makes the system more intelligent and decrease the false alarm rate.
Keywords :
belts; conveyors; fires; fuzzy neural nets; fuzzy reasoning; mining industry; sensor fusion; automatic fire detection technology; coalmines; conveyor belt fire early detection; fuzzy inference; multisensor fire detection system; self-adaptive neural fuzzy network; Alarm systems; Belts; Change detection algorithms; Fires; Fuzzy neural networks; Fuzzy systems; Neural networks; Sensor systems; Signal processing; Temperature;
Conference_Titel :
Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4994-1
DOI :
10.1109/ICIECS.2009.5367076