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

作　　者：Hongzhou Ai Dong Han Xinzhi Wang Quanyi Liu Yue Wang Mengyue Li Pei Zhu 

机构地区：[1]College of Civil Aviation Safety Engineering,Civil Aviation Flight University of China,Guanghan,618307,China [2]CivilAircraf Fire Science and Safety Enginring Key Laboratory of Sichuan,CivilAviation Flight University of China,Guanghan 618307,China [3]School of Computer Engineering and Science,Shanghai University,Shanghai 200444,China [4]Key Laboratory of Civil Aviation Emergency Science&Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210000,China

出　　处：《Journal of Safety Science and Resilience》2024年第2期194-203,共10页安全科学与韧性（英文）

基　　金：This work was funded by the National Science Foundation of China(Grant No.U2033206);the Project of Civil Aircraft Fire Science and Safety Engineering Key Laboratory of Sichuan Province(Grant No.MZ2022KF05,Grant No.MZ2022JB01);the project of Key Laboratory of Civil Aviation Emergency Science&Technology,CAAC(Grant No.NJ2022022,Grant No.NJ2023025);the project of Postgraduate Project of Civil Aviation Flight University of China(Grant No X2023-1);the project of the undergraduate innovation and entrepreneurship training program(Grant No 202210624024);the project of General Programs of the Civil Aviation Flight University of China(Grant No J2020-072).

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.

关 键 词：Deep learning Aircraft cargo compartment Attentionmechanism Firedetection Multi-sourcedata fusion 

分 类 号：X928.7[环境科学与工程—安全科学]