Research on multi-sensor smoke detection method foraircraft cargo compartment

doi:10.16265/j.cnki.issn1003-3033.2019.01.008

Abstract

Abstract: In order to solve the problem of the high false alarm rates of the aircraft cargo compartment fire detectors caused by the dust and water vapor particles in the air, a multi-sensor composite aircraft cargo compartment fire detection method was worked out. Firstly, a composite fire detection device was built, including a temperature sensor, a CO sensor and a dual-wavelength photoelectric smoke sensor. A fire detection system software was designed. Then a large number of true and false fire source experiments were carried out to collect data on the parameter variation features of smoke, temperature and gas during the fire. Finally, the artificial neural network algorithm was used to perform fusion analysis of the collected data. The experimental data show that the alarm accuracy of the multi-sensor detection device embedded with dual-wavelength photoelectric smoke detector is significantly higher than that of traditional fire smoke detectors, and the relative error of interference source identification does not exceed 5.7%.

Key words: aircraft cargo compartment, fire detection, composite detector, dual-wavelength, data fusion, neural network

CLC Number:

X932

HE Yongbo, ZHANG Wenjie, YANG Wei, LI Yongqing. Research on multi-sensor smoke detection method foraircraft cargo compartment[J]. China Safety Science Journal, 2019, 29(1): 43-48.

References

[1] DAVID B. Aircraft cargo compartment smoke detector alarm incidents on U.S.-registered aircraft, 1974-1999[R].DOT/FAA/AR-TN00/29,2000.
[2] JAMES A M, MATTHEW E H, CLARENCE L W. Investigation of multi-sensor algorithms for fire detection[J]. Fire Technology, 2003, 39: 363-382.
[3] DAVID B.Initial development of improved aircraft cargo compartment fire detection certification criteria[C].12^th International Conference on Automatic Fire Detection,2001: 615-629.
[4] THOMAS C, MICHELLE D. Aircraft cargo compartment fire and nuisance source test in the FE/DE[C].12^th International Conference on Automatic Fire Detection,2001: 689-700.
[5] CHEN S J, HOVDE D C, PETERSON K A, et al. Fire detection using smoke and gas sensor[J].Fire Safety Journal,2007,42(8): 507-515.
[6] ADITYANAND G.Aircraft cargo compartment multisensor smoke detection algorithm development[R].DOT/FAA/AR-07/58,2008.
[7] 冯相国. 飞机货舱复合型烟雾探测器设计与研究[D].天津:中国民航大学,2012.
FENG Xiangguo. Design and research of multi-sensor smoke detector in aircraft cargo hold [D].Tianjin: Civil Aviation University of China,2012.
[8] 张辉,石险峰,孙俊岭.双向散射及双波段光电感烟火灾探测器特性研究与设计[D].合肥:合肥工业大学,2007.
ZHANG Hui, SHI Xianfeng, SUN Junling.The research and design of photoele ctric smoke detectors using forward/backward scattered light or double wavelengths lignt[D].Hefei:Hefei University of Technology,2007.
[9] 王殊,邓田,窦征,等.双波长气溶胶粒径传感方法及其火灾烟雾探测器[J].火灾科学,2016,25(4): 224-227.
WANG Shu, DENG Tian, DOU Zheng, et al.Sensing method of aerosol median diameter based on dual wavelength and its application in fire smoke detectors[J].Fire Safety Science,2016,25(4): 224-227.
[10] 孙悟,邓小玖,李耀东,等.双波长抗干扰光电感烟探测机理[J].物理学报,2013,62(3): 1-6.
SUN Wu, DENG Xiaojiu,LI Yaodong, et al.Mechanism of dual-wavelength anti-jamming photoelectric smoke-detection[J].Acta Physica Sinica,2013,62(3): 1-6.
[11] 向淑兰,付尧明,现代飞机货舱火警探测系统研究[J].中国测试技术,2004,20(5): 18-20.
XIANG Shulan, FU Yaoming. The study of the fire detection system for modern aircraft's cargo compartment[J]. China Measurement Technology,2004,30(5): 18-20.
[12] 何永勃,董玉珊,雷剑,等.基于多传感器的飞机火灾探测方法算法研究[J].中国安全科学学报,2017,27(9): 74-79.
HE Yongbo,DONG Yushan,LEI Jian, et al. Research on algorithm of aircraft fire detection method based on multi-sensor[J].China Safety Science Journal,2017,27(9): 74-79.
[13] 汪书苹,王海超,范明豪,等.基于多信息融合的电力火灾综合探测技术研究[J].消防科学与技术,2017,36(2): 213-217.
WANG Shuping,WANG Haichao,FAN Minghao, et al. Research on electric power fire integrated detection technology based on multi-information fusion[J].Fire Science and Technology,2017,36(2): 213-217.
[14] 李杰,李平,谢启苗,等.自适应神经网络在FPSO火灾预警中的应用[J].中国安全科学学报,2017,27(12): 8-13.
LI Jie, LI Ping, XIE Qimiao, et al.Application of adaptive neural network in FPSO fire warning[J].China Safety Science Journal,2017,27(12): 8-13.
[15] 姚畅,钱盛友.基于神经网络的多传感器火灾报警系统[J].计算机工程与应用,2006,42(3): 219-221.
YAO Chang,QIAN Shengyou.Fire alarm system of multi-sensors based on neural network[J].Computer Engineering and Application,2006,42(3): 219-221.