主管:中华人民共和国应急管理部
主办:应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU

消防科学与技术 ›› 2023, Vol. 42 ›› Issue (3): 370-373.

• • 上一篇    下一篇

基于毫米波的民机货舱火灾多参数探测技术研究

邓 力,谢爽爽,贺元骅,刘全义   

  1. (中国民用航空飞行学院 民航安全工程学院,四川 广汉 618307)
  • 出版日期:2023-03-15 发布日期:2023-03-15
  • 作者简介:邓 力(1986- ),男,中国民用航空飞行学院民航安全工程学院副教授,硕士生导师,硕士,主要从事民航安全工程技术、机载火灾探测技术、信息处理及嵌入式开发等研究,四川省广汉市南昌路四段46号,618307。
  • 基金资助:
    国家自然科学基金资助项目(U1933105);中国民用航空飞行学院基金资助项目(J2020-120)

Research on multi parameter detection technology of civil aircraft cargo compartment fire based on millimeter wave

Deng Li, Xie Shuangshuang, He Yuanhua, Liu Quanyi   

  1. (College of Civil Aviation Safety Engineering, Civil Aviation Flight University of China, Sichuan Guanghan 618307, China)
  • Online:2023-03-15 Published:2023-03-15

摘要: 为了解决民机货舱等受限空间内光电火灾探测技术造成误报的问题,设计对烟气环境敏感变化的毫米波谐振腔,建立可实现多参数信号检测的可燃物燃烧试验平台。选取频率偏移量作为火灾烟气探测的主要特征参数,进行多参数数据处理与信息融合,采用基于YOLOv5网络模型的机器学习算法对火灾探测红外图像及烟气数据进行训练学习,评估分析其分类性能,并试验分析了探测系统对燃烧物种类及燃烧类型的识别效果。结果表明:多参数融合的火灾探测系统的烟气误报率、漏检率、探测响应时间、燃烧物种类识别率、阴燃火识别率等性能均优于现有单一传感烟感探测器。可见,采用基于频率偏移探测的多参数融合探测系统进行飞机货舱火灾探测的方法是有效的,能够提高火灾探测的准确率,减少误报漏报的发生。

关键词: 毫米波谐振腔, 火灾探测, 频率偏移, 多参数融合, 机器学习

Abstract: Abstract: In order to solve the false alarm caused by photoelectric fire detection technology in confined spaces such as civil aircraft cargo hold, a millimeter wave resonator sensitive to the change of flue gas environment is designed, and a combustible combustion experimental platform which can realize multi parameter signal detection is established. The frequency offset is selected as the main characteristic parameter of fire smoke detection for multi parameter data processing and information fusion. The machine learning algorithm based on YOLOv5 network model is used to train and learn the infrared image and smoke data of fire detection, evaluate and analyze their classification performance, and experimentally analyze the recognition effect of the detection system on the types of combustibles and combustion types. The results show that the multi parameter fusion fire detection system is superior to the existing single sensing smoke detector in smoke false alarm rate, missed detection rate, detection response time, combustion species identification rate and smoldering fire identification rate. It can be seen that the method of aircraft cargo compartment fire detection based on multi parameter fusion detection system based on frequency offset detection is effective, which can improve the accuracy of fire detection and reduce the occurrence of false alarm and missing alarm.

Key words: Key words: millimeter wave resonator, fire detection, frequency offset, multi parameter fusion, machine learning