关于利用多变量航空时间序列数据进行异常检测的时态融合变换器探索

IF 2.1 3区 工程技术 Q2 ENGINEERING, AEROSPACE
Bulent Ayhan, Erik P. Vargo, Huang Tang
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引用次数: 0

摘要

在这项工作中,我们利用 MITRE 公司运输数据平台 (TDP) 的线程轨迹数据和数字飞行数据,探索了使用基于变压器的时间序列预测架构(称为时态融合变压器 (TFT))进行异常检测的可行性。TFT 架构具有灵活性,既可包含时变多变量数据,也可包含来自多模态数据源的分类数据,并可进行单输出或多输出预测。对于异常检测,我们不是训练 TFT 模型来预测特定航空安全事件的结果,而是训练 TFT 模型来学习名义行为。在进行评估时,TFT 模型对相关输出飞行参数的未来预测与观察到的时间序列数据之间的任何重大偏差都会被视为异常。在概念验证演示中,我们使用不稳定方法 (UA) 作为异常事件。这种带有标称行为学习的异常检测方法可用于开发飞行分析,以识别历史飞行数据中新出现的安全隐患,并有可能用作机载预警系统,在飞行过程中为飞行员提供帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the Exploration of Temporal Fusion Transformers for Anomaly Detection with Multivariate Aviation Time-Series Data
In this work, we explored the feasibility of using a transformer-based time-series forecasting architecture, known as the Temporal Fusion Transformer (TFT), for anomaly detection using threaded track data from the MITRE Corporation’s Transportation Data Platform (TDP) and digital flight data. The TFT architecture has the flexibility to include both time-varying multivariate data and categorical data from multimodal data sources and conduct single-output or multi-output predictions. For anomaly detection, rather than training a TFT model to predict the outcomes of specific aviation safety events, we train a TFT model to learn nominal behavior. Any significant deviation of the TFT model’s future horizon forecast for the output flight parameters of interest from the observed time-series data is considered an anomaly when conducting evaluations. For proof-of-concept demonstrations, we used an unstable approach (UA) as the anomaly event. This type of anomaly detection approach with nominal behavior learning can be used to develop flight analytics to identify emerging safety hazards in historical flight data and has the potential to be used as an on-board early warning system to assist pilots during flight.
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来源期刊
Aerospace
Aerospace ENGINEERING, AEROSPACE-
CiteScore
3.40
自引率
23.10%
发文量
661
审稿时长
6 weeks
期刊介绍: Aerospace is a multidisciplinary science inviting submissions on, but not limited to, the following subject areas: aerodynamics computational fluid dynamics fluid-structure interaction flight mechanics plasmas research instrumentation test facilities environment material science structural analysis thermophysics and heat transfer thermal-structure interaction aeroacoustics optics electromagnetism and radar propulsion power generation and conversion fuels and propellants combustion multidisciplinary design optimization software engineering data analysis signal and image processing artificial intelligence aerospace vehicles'' operation, control and maintenance risk and reliability human factors human-automation interaction airline operations and management air traffic management airport design meteorology space exploration multi-physics interaction.
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