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 

作者机构：College of Civil Aviation Safety EngineeringCivil Aviation Flight University of ChinaGuanghan618307China CivilAircraf Fire Science and Safety Enginring Key Laboratory of SichuanCivilAviation Flight University of ChinaGuanghan 618307China School of Computer Engineering and ScienceShanghai UniversityShanghai 200444China Key Laboratory of Civil Aviation Emergency Science&TechnologyNanjing University of Aeronautics and AstronauticsNanjing 210000China 

出 版 物：《Journal of Safety Science and Resilience》 (安全科学与韧性（英文）)

年 卷 期：2024年第5卷第2期

页      面：194-203页

核心收录：

学科分类：08[工学] 0838[工学-公安技术] 

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) 

主　　题：Deep learning Aircraft cargo compartment Attentionmechanism Firedetection Multi-sourcedata fusion 

摘      要：The implementation of early and accurate detection of aircraft cargo compartment fire is of great significance to ensure flight *** current airborne fire detection technology mostly relies on single-parameter smoke detection using infrared *** often results in a high false alarm rate in complex air transportation *** traditional deep learning model struggles to effectively address the issue of long-term dependency in multivariate fire *** paper proposes a multi-technology collaborative fire detection method based on an improved transformers ***-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 *** 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 ***,the output results of the two models are fused through the gate *** research results show that,compared with the traditional single-feature detection technology,the multi-technology collaborative fire detection method can better capture fire *** 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.