Early fire detection technology based on improved transformers in aircraft cargo compartments

扫码查看

原文链接

万方数据

外文摘要：The implementation of early and accurate detection of aircraft cargo compartment fire is of great significance to ensure flight safety.The current airborne fire detection technology mostly relies on single-parameter smoke detection using infrared light.This often results in a high false alarm rate in complex air transportation envi-ronments.The traditional deep learning model struggles to effectively address the issue of long-term dependency in multivariate fire information.This paper proposes a multi-technology collaborative fire detection method based on an improved transformers model.Dual-wavelength optical sensors,flue gas analyzers,and other equipment are used to carry out multi-technology collaborative detection methods and characterize various feature dimensions of fire to improve detection accuracy.The improved Transformer model which integrates the self-attention mechanism and position encoding mechanism is applied to the problem of long-time series modeling of fire information from a global perspective,which effectively solves the problem of gradient disappearance and gradient explosion in traditional RNN(recurrent neural network)and CNN(convolutional neural network).Two different multi-head self-attention mechanisms are used to classify and model multivariate fire information,respectively,which solves the problem of confusing time series modeling and classification modeling in dealing with multivariate classification tasks by a single attention mechanism.Finally,the output results of the two models are fused through the gate mechanism.The research results show that,compared with the traditional single-feature detection technology,the multi-technology collaborative fire detection method can better capture fire information.Compared with the traditional deep learning model,the multivariate fire pre-diction model constructed by the improved Transformer can better detect fires,and the accuracy rate is 0.995.

外文关键词：

Deep learningAircraft cargo compartmentAttention mechanismFire detectionMulti-source data fusion

作者：

Hong-zhou Ai、Dong Han、Xin-zhi Wang、Quan-yi Liu、Yue Wang、Meng-yue Li、Pei Zhu

展开 >

作者单位：

College of Civil Aviation Safety Engineering,Civil Aviation Flight University of China,Guanghan 618307,China

Civil Aircraft Fire Science and Safety Engineering Key Laboratory of Sichuan,Civil Aviation Flight University of China,Guanghan 618307,China

School of Computer Engineering and Science,Shanghai University,Shanghai,200444,China

College of Civil Aviation Safety Engineering,Civil Aviation Flight University of China,Guanghan,618307,China

Key Laboratory of Civil Aviation Emergency Science&Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210000,China

展开 >

出版年：

2024

DOI：

10.1016/j.jnlssr.2024.03.003

安全科学与韧性(英文)

ISSN：

年,卷(期)：2024.(2)