基于改进的飞机货舱变压器的早期火灾探测技术

IF 3.7 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Hong-zhou Ai , Dong Han , Xin-zhi Wang , Quan-yi Liu , Yue Wang , Meng-yue Li , Pei Zhu
{"title":"基于改进的飞机货舱变压器的早期火灾探测技术","authors":"Hong-zhou Ai ,&nbsp;Dong Han ,&nbsp;Xin-zhi Wang ,&nbsp;Quan-yi Liu ,&nbsp;Yue Wang ,&nbsp;Meng-yue Li ,&nbsp;Pei Zhu","doi":"10.1016/j.jnlssr.2024.03.003","DOIUrl":null,"url":null,"abstract":"<div><p>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 environments. 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 prediction model constructed by the improved Transformer can better detect fires, and the accuracy rate is 0.995.</p></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666449624000197/pdfft?md5=ae8ef9b8111b0ce9fd4d6ed8f06a3a5e&pid=1-s2.0-S2666449624000197-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Early fire detection technology based on improved transformers in aircraft cargo compartments\",\"authors\":\"Hong-zhou Ai ,&nbsp;Dong Han ,&nbsp;Xin-zhi Wang ,&nbsp;Quan-yi Liu ,&nbsp;Yue Wang ,&nbsp;Meng-yue Li ,&nbsp;Pei Zhu\",\"doi\":\"10.1016/j.jnlssr.2024.03.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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 environments. 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 prediction model constructed by the improved Transformer can better detect fires, and the accuracy rate is 0.995.</p></div>\",\"PeriodicalId\":62710,\"journal\":{\"name\":\"安全科学与韧性(英文)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-04-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666449624000197/pdfft?md5=ae8ef9b8111b0ce9fd4d6ed8f06a3a5e&pid=1-s2.0-S2666449624000197-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"安全科学与韧性(英文)\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666449624000197\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"安全科学与韧性(英文)","FirstCategoryId":"1087","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666449624000197","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0

摘要

对飞机货舱火灾实施早期准确探测对确保飞行安全具有重要意义。目前的机载火灾探测技术大多依赖于利用红外光进行单参数烟雾探测。在复杂的航空运输环境中,这往往会导致较高的误报率。传统的深度学习模型难以有效解决多元火灾信息的长期依赖性问题。本文基于改进的变压器模型,提出了一种多技术协同火灾探测方法。利用双波长光学传感器、烟气分析仪等设备开展多技术协同探测方法,表征火灾的各种特征维度,提高探测精度。从全局角度出发,将集成了自注意力机制和位置编码机制的改进型变压器模型应用于火灾信息的长时间序列建模问题,有效解决了传统 RNN(循环神经网络)和 CNN(卷积神经网络)中梯度消失和梯度爆炸的问题。采用两种不同的多头自注意机制分别对多元火灾信息进行分类和建模,解决了单一注意机制在处理多元分类任务时混淆时间序列建模和分类建模的问题。最后,两个模型的输出结果通过门机制进行融合。研究结果表明,与传统的单一特征探测技术相比,多技术协同火灾探测方法能更好地捕捉火灾信息。与传统的深度学习模型相比,改进后的 Transformer 构建的多元火灾预测模型能更好地探测火灾,准确率达到 0.995。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Early fire detection technology based on improved transformers in aircraft cargo compartments

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 environments. 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 prediction model constructed by the improved Transformer can better detect fires, and the accuracy rate is 0.995.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
安全科学与韧性(英文)
安全科学与韧性(英文) Management Science and Operations Research, Safety, Risk, Reliability and Quality, Safety Research
CiteScore
8.70
自引率
0.00%
发文量
0
审稿时长
72 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信