Real-time identification of precursors in commercial aviation using multiple-instance learning

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Advanced Engineering Informatics Pub Date : 2024-10-01 DOI:10.1016/j.aei.2024.102856
Zhiwei Xiang , Zhenxing Gao , Yansong Gao , Yangyang Zhang , Runhao Zhang
{"title":"Real-time identification of precursors in commercial aviation using multiple-instance learning","authors":"Zhiwei Xiang ,&nbsp;Zhenxing Gao ,&nbsp;Yansong Gao ,&nbsp;Yangyang Zhang ,&nbsp;Runhao Zhang","doi":"10.1016/j.aei.2024.102856","DOIUrl":null,"url":null,"abstract":"<div><div>This research pioneers the application of precursor concepts to preemptively identify and prevent aviation safety incidents using Machine Learning (ML). Airlines and governing organizations, such as the Federal Aviation Administration (FAA) in the United States, have been trying to prevent safety incidents during routine operations. However, this task is challenging due to the lack of timestep-wise event annotation in flights and the complexity involved in the timely identification of incidents prior to their occurrence. To address these issues, we propose a real-time precursor identification methodology combining Multiple-Instance Learning (MIL) and feature-based Knowledge Distillation (KD) learning. Our two-stage approach, involving deep MIL for labeling and a KD-based model for real-time warnings, demonstrates state-of-the-art performance and a time delay of 2.99ms using a dataset of 23,549 real flights. Further experiments using t-distributed Stochastic Neighbor Embedding (t-SNE) and occlusion method confirm our model’s transparency, enabling the generation of reliable quantitative precursor scores and facilitating reasoning about the causes of safety incidents at the parameter level. Additionally, statistical analysis of precursors reveals varying evolution times for different safety events, which indicates that pilots have at least 8 s to react after receiving a warning. In conclusion, our research provides a theoretical foundation and technical support for the next generation of online risk warning systems, enhancing flight safety and offering a pathway towards more intelligent and secure flight operations.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":null,"pages":null},"PeriodicalIF":8.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Engineering Informatics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1474034624005044","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

This research pioneers the application of precursor concepts to preemptively identify and prevent aviation safety incidents using Machine Learning (ML). Airlines and governing organizations, such as the Federal Aviation Administration (FAA) in the United States, have been trying to prevent safety incidents during routine operations. However, this task is challenging due to the lack of timestep-wise event annotation in flights and the complexity involved in the timely identification of incidents prior to their occurrence. To address these issues, we propose a real-time precursor identification methodology combining Multiple-Instance Learning (MIL) and feature-based Knowledge Distillation (KD) learning. Our two-stage approach, involving deep MIL for labeling and a KD-based model for real-time warnings, demonstrates state-of-the-art performance and a time delay of 2.99ms using a dataset of 23,549 real flights. Further experiments using t-distributed Stochastic Neighbor Embedding (t-SNE) and occlusion method confirm our model’s transparency, enabling the generation of reliable quantitative precursor scores and facilitating reasoning about the causes of safety incidents at the parameter level. Additionally, statistical analysis of precursors reveals varying evolution times for different safety events, which indicates that pilots have at least 8 s to react after receiving a warning. In conclusion, our research provides a theoretical foundation and technical support for the next generation of online risk warning systems, enhancing flight safety and offering a pathway towards more intelligent and secure flight operations.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用多实例学习实时识别商用航空中的前兆物
本研究率先应用前兆概念,利用机器学习(ML)技术预先识别和预防航空安全事故。航空公司和管理机构,如美国联邦航空管理局(FAA),一直在努力预防日常运营中的安全事故。然而,由于缺乏按时间顺序排列的飞行事件注释,以及在事件发生前及时识别事件的复杂性,这项任务极具挑战性。为了解决这些问题,我们提出了一种结合多实例学习(MIL)和基于特征的知识蒸馏(KD)学习的实时前兆识别方法。我们的两阶段方法包括用于标记的深度 MIL 和用于实时警告的基于 KD 的模型,在使用 23,549 次真实航班的数据集时,表现出了一流的性能和 2.99 毫秒的时间延迟。使用 t 分布随机邻域嵌入(t-SNE)和闭塞方法进行的进一步实验证实了我们模型的透明度,能够生成可靠的定量前兆分数,并有助于在参数级别推理安全事故的原因。此外,对前兆的统计分析显示,不同安全事件的演变时间各不相同,这表明飞行员在收到警告后至少有 8 秒钟的反应时间。总之,我们的研究为下一代在线风险预警系统提供了理论基础和技术支持,提高了飞行安全,为实现更智能、更安全的飞行操作提供了途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
期刊最新文献
A method for constructing an ergonomics evaluation indicator system for community aging services based on Kano-Delphi-CFA: A case study in China A temperature-sensitive points selection method for machine tool based on rough set and multi-objective adaptive hybrid evolutionary algorithm Enhancing EEG artifact removal through neural architecture search with large kernels Optimal design of an integrated inspection scheme with two adjustable sampling mechanisms for lot disposition A novel product shape design method integrating Kansei engineering and whale optimization algorithm
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1