用AI分析航空事故报告，自动分类损伤等级和飞行阶段。
Utilizing AI for Aviation Post-Accident Analysis Classification
- 结合NLP与深度学习，从事故报告中提取关键信息。
- 在NTSB、ATSB等数据集上实现高准确率分类，提升分析效率。
- 适合航空安全研究者及智能监管系统开发者使用。
航空安全报告中的文本数据量庞大，传统分析难以及时高效。本文探讨人工智能（AI）尤其是自然语言处理（NLP）技术在自动化提取报告价值信息方面的应用，旨在提升航空安全水平。研究聚焦于利用NLP与深度学习对事故报告进行分类，识别飞机受损程度及事故发生阶段。同时，采用主题建模（TM）挖掘报告中潜在的主题结构，发现重复性模式与可改进的安全领域。对比分析了多种深度学习模型与TM方法在国家运输安全委员会（NTSB）、澳大利亚交通安全局（ATSB）以及航空安全网络（ASN）数据集上的表现，讨论了数据规模与来源对分析准确性的影响。结果表明，NLP、深度学习及主题建模均可显著提升航空安全分析的效率与精度，为更主动的安全管理与风险防控提供支持。
原文摘要 · Abstract (English)
The volume of textual data available in aviation safety reports presents a challenge for timely and accurate analysis. This paper examines how Artificial Intelligence (AI) and, specifically, Natural Language Processing (NLP) can automate the process of extracting valuable insights from this data, ultimately enhancing aviation safety. The paper reviews ongoing efforts focused on the application of NLP and deep learning to aviation safety reports, with the goal of classifying the level of damage to an aircraft and identifying the phase of flight during which safety occurrences happen. Additionally, the paper explores the use of Topic Modeling (TM) to uncover latent thematic structures within aviation incident reports, aiming to identify recurring patterns and potential areas for safety improvement. The paper compares and contrasts the performance of various deep learning models and TM techniques applied to datasets from the National Transportation Safety Board (NTSB) and the Australian Transport Safety Bureau (ATSB), as well as the Aviation Safety Network (ASN), discussing the impact of dataset size and source on the accuracy of the analysis. The findings demonstrate that both NLP and deep learning, as well as TM, can significantly improve the efficiency and accuracy of aviation safety analysis, paving the way for more proactive safety management and risk mitigation strategies.
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