基于贝叶斯优化LSTM神经网络的飞机货舱火源定位

Fire source localization for long short-term memory networks based on Bayesian optimization

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摘要民航飞机货舱火灾多发于高空低温低压的环境,对飞机安全飞行造成了巨大的威胁。为快速定位货舱火灾源点和采取针对性区域灭火措施,提出一种基于贝叶斯优化(BO)的长短期记忆(LSTM)神经网络火源定位模型(BO-LSTM)。该模型使用LSTM神经网络充分挖掘多种火灾特征时序数据(烟雾、温度、CO浓度)与火灾源点的时空关联特性,同时采用贝叶斯算法搜寻LSTM神经网络的最优超参数组合以提高模型的鲁棒性和准确性。通过仿真研究验证BO-LSTM模型,使用Pyrosim火灾模拟软件以1∶1比例建立了8个常用民航飞机货舱模型,并在每个模型中随机选取10个火源点进行低温低压环境的火灾仿真。实验结果表明:所建模型预测火源中心点距离实际火源中心点的直线距离误差皆小于0.1m,预测火源二维坐标皆处于真实火源的范围内。贝叶斯优化过的LSTM神经网络极大提高了传统LSTM神经网络的性能,适用于低温低压状态下的飞机货舱火源定位。 Civil aviation aircraft cargo compartment fires often occur in high-altitude,low-temperature and lowpressure environments,posing a significant threat to aircraft safety.A Bayesian optimization(BO)based long shortterm memory(LSTM)neural network model(BO-LSTM)is proposed to quickly locate fire source and take specific means for fire suppression.In BO-LSTM model,time series data are fully analyzed to investigate the spatiotemporal correlation between fire characteristics(smoke,temperature,CO concentration)and fire source by using LSTM network.Meanwhile,the Bayesian algorithm is employed to search for the optimal LSTM network hyperparameter combination to improve model robustness and accuracy.In this paper,simulation study was used to validate the proposed BO-LSTM method:8 widely used aircraft cargo compartment models were built by Pyrosim®fire simulation software at a scale of 1∶1,and 10 fire source positions were randomly selected in each cargo compartment model to simulate fire data at low-temperature and low-pressure conditions.The experimental results demonstrate that the distance error between predicted fire location and true fire source was less than 0.1 meters,and the predicted twodimensional fire source localized within the true fire source range.The performance of LSTM neural network has been improved dramatically by the Bayesian optimization method and make it a suitable tool for aircraft cargo compartment fire source localization at the environment of low-temperature and low-pressure.

作者张伟常本强杨旭熊枭 ZHANG Wei;CHANG Benqiang;YANG Xu;XIONG Xiao(School of Transportation Science and Engineering,Civil Aviation University of China,Tianjin 300300,China;School of Safety Science and Engineering,Civil Aviation University of China,Tianjin 300300,China;Shenzhen Urban Public Safety Technology Research Institute Co.,Ltd.,Shenzhen 518024,China)

机构地区中国民航大学交通科学与工程学院中国民航大学安全科学与工程学院深圳市城市公共安全技术研究院有限公司

出处《北京航空航天大学学报》北大核心 2025年第9期2979-2986,共8页 Journal of Beijing University of Aeronautics and Astronautics

基金中央高校基本科研业务费专项资金(3122020048)。

关键词飞机货舱低温低压火源定位贝叶斯优化 LSTM神经网络 Pyrosim软件 aircraft cargo hold low temperature and low pressure fire source localization Bayesian optimization LSTM neural network Pyrosim

分类号 V244.12 [航空宇航科学与技术—飞行器设计]

作者简介通信作者:熊枭,E-mail:xiongxiao@szsti.org。