DocumentCode :
145209
Title :
Aspirating fire detection system with high sensitivity and multi-parameter
Author :
Liu Shixing ; Luo Xinxin ; Yao Wei ; Chen Changzheng ; Yin Kun ; Yi Maoxiang ; Hu Haibing ; Zhang Yongming
Author_Institution :
Sch. of Electron. Sci. & Appl. Phys., Hefei Univ. of Technol., Hefei, China
Volume :
1
fYear :
2014
fDate :
26-28 April 2014
Firstpage :
400
Lastpage :
404
Abstract :
A kind of ultra-early fire detection system with high sensitivity and multi-parameter is introduced. The hardware of the system comprises smoke detecting module, CO gas detecting module, temperature detecting module and processing module. Optical measurement room of smoke detecting module is designed specially; it serves as a collimating and beam expanding unit as well as laser trap. It can detect the smoke of ultra-low concentration in the sample air by adopting high sensitivity photovoltaic cell and optimized amplifying circuit. The CO sensor has the characteristics of high sensitivity and quick response. It can detect CO gas of low concentration in the short time. Digital temperature sensor with high performance is applied in temperature detecting module. By adding the measurement parameters using the data fusion technology algorithm based on grey fuzzy neural network, the system has high precision and sensitivity. This system overcomes the weakness of some detecting system which only detects smoke concentration. Results of experiment show that: the detection sensitivities of smoke, CO gas and temperature are 0.005%obs/m, 0.5ppm and 0.1°C. The system can judge a fire occur and give an alarm in 15 seconds.
Keywords :
carbon compounds; chemical variables measurement; circuit optimisation; computerised instrumentation; digital instrumentation; fires; fuzzy neural nets; gas sensors; grey systems; photovoltaic cells; sensor fusion; smoke detectors; temperature sensors; CO gas detecting module; CO sensor; amplifying circuit optimization; beam expanding unit; collimating unit; data fusion technology algorithm; digital temperature sensor; fire detection system; grey fuzzy neural network; laser trap; multiparameter analysis; optical measurement room; photovoltaic cell; processing module; sample air; smoke concentration detection; smoke detecting module; temperature detecting module; aspirating smoke detection; grey fuzzy neural network; high sensitivity; multi-parameter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science, Electronics and Electrical Engineering (ISEEE), 2014 International Conference on
Conference_Location :
Sapporo
Print_ISBN :
978-1-4799-3196-5
Type :
conf
DOI :
10.1109/InfoSEEE.2014.6948140
Filename :
6948140
Link To Document :
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