飞机货舱复合烟雾探测方法研究

doi:10.16265/j.cnki.issn1003-3033.2019.01.008

中国安全科学学报 ›› 2019, Vol. 29 ›› Issue (1): 43-48.doi: 10.16265/j.cnki.issn1003-3033.2019.01.008

飞机货舱复合烟雾探测方法研究

何永勃副教授, 张文杰, 杨伟, 李勇庆

中国民航大学电子信息与自动化学院,天津 300300

收稿日期:2018-10-11 修回日期:2018-12-03 出版日期:2019-01-28 发布日期:2020-11-23
作者简介:何永勃 (1971—),男,陕西蒲城人,博士,副教授,主要从事航空检测技术及智能化仪表方面的研究。
基金资助:
民航科技项目基金资助(MHRD20150220);“973”计划基金资助(2012CB720100)。

Research on multi-sensor smoke detection method foraircraft cargo compartment

HE Yongbo, ZHANG Wenjie, YANG Wei, LI Yongqing

School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300

Received:2018-10-11 Revised:2018-12-03 Online:2019-01-28 Published:2020-11-23

摘要/Abstract

摘要： 为解决目前飞机货舱火警探测装置易受空气中灰尘和水蒸气颗粒干扰造成误报率极高的问题,提出一种复合多传感器的飞机货舱火灾探测方法。首先搭建包括温度传感器、CO传感器和双波长光电式烟雾传感器的复合式火灾探测装置,设计火灾探测系统软件;然后进行真假火源试验,采集火灾过程中烟雾、温度、气体的变化特征参数;最后采用人工神经网络算法对采集到的数据进行融合分析。结果表明:嵌入双波长光电式烟雾探测器的多传感器探测装置报警正确率比传统火灾烟雾探测器有大幅度提高,干扰源识别相对误差不超过5.7%。

关键词: 飞机货舱, 火警探测, 复合式探测器, 双波长, 数据融合, 神经网络

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

中图分类号:

X932

何永勃, 张文杰, 杨伟, 李勇庆. 飞机货舱复合烟雾探测方法研究[J]. 中国安全科学学报, 2019, 29(1): 43-48.

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.

参考文献

[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.

飞机货舱复合烟雾探测方法研究

Research on multi-sensor smoke detection method foraircraft cargo compartment

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	何永勃, 曹祝兵. 基于IPSO-BP模型的火灾气体传感器气压补偿算法^*[J]. 中国安全科学学报, 2022, 32(2): 107-114.
[2]	石娟, 常丁懿, 郑鹏. 基于BP神经网络的建筑工人不安全行为预警模型^*[J]. 中国安全科学学报, 2022, 32(1): 27-33.
[3]	郑度奎, 程远鹏, 李昊燃, 何天隆. IAFSA-GRNN在油田集输管道CO₂腐蚀速率预测中的应用^*[J]. 中国安全科学学报, 2022, 32(1): 110-117.
[4]	岳仁田, 韩蒙. 基于四诊法的管制席安全运行状态诊断^*[J]. 中国安全科学学报, 2021, 31(9): 52-59.
[5]	王伟, 王坚, 王丽斌, 杨君涛, 张永丰. 动态变压货舱火行为及烧穿防火内衬层能力研究^*[J]. 中国安全科学学报, 2021, 31(9): 99-105.
[6]	杨黎霞, 许茂增, 陈仁祥. 数据样本有限的交通恐怖袭击行为识别^*[J]. 中国安全科学学报, 2021, 31(8): 30-37.
[7]	古莹奎, 刘平, 林忠海, 邱光琦. 基于Kurtogram与DSCN的滚动轴承故障诊断方法[J]. 中国安全科学学报, 2021, 31(6): 99-105.
[8]	张凯, 张科, 李昆. 主元分析-神经网络岩爆等级预测模型[J]. 中国安全科学学报, 2021, 31(3): 96-104.
[9]	王海飙, 陈海超, 李蕊, 于贺, 赵天怡. 城市应急避难场所管理体系构建与应用^*[J]. 中国安全科学学报, 2021, 31(12): 153-159.
[10]	毕傲睿, 骆正山, 宋莹莹, 张新生. 内腐蚀海底管道剩余强度的FOA-GRNN模型[J]. 中国安全科学学报, 2020, 30(6): 78-83.
[11]	肖鹏, 谢行俊, 双海清, 刘朝阳, 王海宁, 徐经苍. 基于KPCA-CMGANN算法的瓦斯涌出量预测研究[J]. 中国安全科学学报, 2020, 30(5): 39-47.
[12]	赵欣然, 张琪, 王卫东, 徐志强. 可燃性粉尘云的图像检测方法[J]. 中国安全科学学报, 2020, 30(4): 8-13.
[13]	宋高峰, 张延兵, 孙培培, 沈硕勋, 王志荣. 大型储罐声发射技术下的安全评价方法[J]. 中国安全科学学报, 2020, 30(3): 60-66.
[14]	戴健非, 杨鹏, 诸利一, 郭攀, 贯怀光. 集成PCA和LSTM神经网络的浸润线预测方法[J]. 中国安全科学学报, 2020, 30(3): 94-101.
[15]	黄平, 文超, 李忠灿, 杨宇翔, 彭其渊. 高速铁路列车晚点时间实时预测的神经网络模型^*[J]. 中国安全科学学报, 2019, 29(S1): 20-26.