基于时序大数据的飞行安全状态评估方法综述

杨洁; 唐荻音; 马泽珊; 刘宝鼎; 贡欣宇; 文正旭; 于劲松; 卢劲鹏; 康锐; 韩丹阳; 陶来发; 冯灿; 刘涛

doi:10.16383/j.aas.c250449

基于时序大数据的飞行安全状态评估方法综述

doi: 10.16383/j.aas.c250449 cstr: 32138.14.j.aas.c250449

杨洁^1,,
唐荻音^2,,
马泽珊^3,,
刘宝鼎^2,,
贡欣宇^2,,
文正旭^3,,
于劲松^2,,
卢劲鹏^3,,
康锐^1,,
韩丹阳^1,,
陶来发^1,,
冯灿^4,,
刘涛^4,

1.
杭州市北京航空航天大学国际创新研究院(北京航空航天大学国际创新学院) 杭州 311115
2.
北京航空航天大学自动化科学与电气工程学院北京 100191
3.
北京航空航天大学中法工程师学院北京 100191
4.
中国商飞上海飞机试飞工程有限公司上海 201323

基金项目: 杭州市北航国际创新研究院博士后科研专项经费(2025BKZ059), 国家自然科学基金(52375074)资助

详细信息

作者简介:
杨洁：杭州市北京航空航天大学国际创新研究院(北京航空航天大学国际创新学院)博士后. 主要研究方向为航空器监测与检测, 飞行状态评估, 可解释异常检测与预测. E-mail: megan_yj@buaa.edu.cn

唐荻音：北京航空航天大学自动化科学与电气工程学院副教授. 主要研究方向为故障诊断, 退化建模以及视情维护. E-mail: tangdiyin@buaa.edu.cn

马泽珊：北京航空航天大学中法工程师学院硕士研究生. 主要研究方向为航空器监测与检测. E-mail: 21241024@buaa.edu.cn

刘宝鼎：北京航空航天大学自动化科学与电气工程学院博士研究生. 主要研究方向为故障诊断, 视情维护. E-mail: 16241094@buaa.edu.cn

贡欣宇：北京航空航天大学自动化科学与电气工程学院硕士研究生. 主要研究方向为预测与健康管理. E-mail: gxyfred@buaa.edu.cn

文正旭：北京航空航天大学中法工程师学院硕士研究生. 主要研究方向为飞行状态评估. E-mail: sy2224137@buaa.edu.cn

于劲松：北京航空航天大学自动化科学与电气工程学院教授. 主要研究方向为预测与健康管理, 自动化测试. 本文通信作者. E-mail: yujs@buaa.edu.cn

卢劲鹏：北京航空航天大学中法工程师学院硕士研究生. 主要研究方向为航空器监测与检测, 预测与健康管理. E-mail: jinpeng_lu@buaa.edu.cn

康锐：杭州市北京航空航天大学国际创新研究院(北京航空航天大学国际创新学院)教授. 主要研究方向为确信可靠性理论. E-mail: kangrui@buaa.edu.cn

韩丹阳：杭州市北京航空航天大学国际创新研究院(北京航空航天大学国际创新学院)博士后. 主要研究方向为故障诊断, 退化建模, 视情维护, 预测与健康管理. E-mail: hdy_daniel@buaa.edu.cn

陶来发：杭州市北京航空航天大学国际创新研究院(北京航空航天大学国际创新学院)研究员. 主要研究方向为故障诊断与预测, 健康状态评估, 复杂系统健康管理. E-mail: taolaifa@buaa.edu.cn

冯灿：中国商飞上海飞机试飞工程有限公司试飞测试技术二级专业总师, 测试工程部部长, 技术创新中心主任. 主要研究方向为试飞测试工程. E-mail: fengcan@comac.cc

刘涛：中国商飞上海飞机试飞工程有限公司技术创新中心主任部门助理. 主要研究方向为试飞测试数据处理. E-mail: liutao2@comac.cc

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出版历程
- 收稿日期: 2025-09-03
- 录用日期: 2026-01-29
- 网络出版日期: 2026-03-13
- 刊出日期: 2026-09-01

Review of Flight Safety State Assessment Methods Based on Large-scale Time-series Data

YANG Jie^1
,,
TANG Di-Yin^2
,,
MA Ze-Shan^3
,,
LIU Bao-Ding^2
,,
GONG Xin-Yu^2
,,
WEN Zheng-Xu^3
,,
YU Jin-Song^2
,,
LU Jin-Peng^3
,,
KANG Rui^1
,,
HAN Dan-Yang^1
,,
TAO Lai-Fa^1
,,
FENG Can^4
,,
LIU Tao^4
,

1.
Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115
2.
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191
3.
Sino-French Engineer School, Beihang University, Beijing 100191
4.
COMAC Shanghai Aircraft Flight Test Co., Ltd., Shanghai 201323

Funds: Supported by Postdoctoral Research Funding of Hangzhou International Innovation Institute of Beihang University (2025BKZ059) and National Natural Science Foundation of China (52375074)

More Information

Author Bio:
YANG Jie　Postdoctor at the Hangzhou International Innovation Institute, Beihang University. Her research interests include aircraft monitoring and detection, flight state assessment, interpretable anomaly detection and prediction

TANG Di-Yin　Associate professor at the School of Automation Science and Electrical Engineering, Beihang University. Her research interests include fault diagnosis, degradation-based modeling, and condition-based mainten-ance

MA Ze-Shan　Master student at the Sino-French Engineer School, Beihang University. Her research interests include aircraft monitoring and detection

LIU Bao-Ding　Ph.D. candidate at the School of Automation Science and Electrical Engineering, Beihang University. His research interests include fault diagnosis and condition-based maintenance

GONG Xin-Yu　Master student at the School of Automation Science and Electrical Engineering, Beihang University. His research interests include prognostics and health management

WEN Zheng-Xu　Master student at the Sino-French Engineer School, Beihang University. His main research interest is flight state assessment

YU Jin-Song　Professor at the School of Automation Science and Electrical Engineering, Beihang University. His research interests include prognostics and health management, and automatic testing. Corresponding author of this paper

LU Jin-Peng　Master student at the Sino-French Engineer School, Beihang University. His research interests include aircraft monitoring and detection, prognostics and health management

KANG Rui　Professor at the Hangzhou International Innovation Institute, Beihang University. His main research interest is belief reliability theory

HAN Dan-Yang　Postdoctor at the Hangzhou International Innovation Institute, Beihang University. His research interests include fault diagnosis, degradation-based modeling, condition-based maintenance, prognostics and health management

TAO Lai-Fa　Researcher at the Hangzhou International Innovation Institute, Beihang University. His research interests include fault diagnosis and prognostics, health state assessment, and health management for complex systems

FENG Can　Level-2 chief technical specialist for flight test, director of the test engineering department, and director of the Technology Innovation Center at the COMAC Shanghai Aircraft Flight Test Co., Ltd. His main research interest is flight test engineering

LIU Tao　Assistant to the department director of the Technology Innovation Center at the COMAC Shanghai Aircraft Flight Test Co., Ltd. His main research interest is flight test data processing

摘要

摘要: 随着全球航班规模与运行密度提升, 传统人工监控方法在应对复杂动态飞行环境方面逐渐显现出局限性. 近年来, 数据驱动的智能监控方法成为提升飞行安全与运行效率的研究热点. 本文对基于时序大数据的飞行安全状态评估方法进行综述, 从飞行数据的时序特性出发, 梳理三类面向不同时间维度的关键评估方法: 异常检测、征兆挖掘以及趋势跟踪与预测, 涵盖飞行异常状态的识别与潜在风险事件的早期预警与预测. 首先, 对上述三类评估方法进行系统定义, 可覆盖“过去−现在−未来”的安全保障. 其次, 介绍各类方法的代表性研究进展与存在问题; 以高风险进近阶段为典型场景, 分析评估方法的应用现状与协同机制, 并回顾其技术支撑, 包括数据通信和软件平台. 最后, 总结当前挑战和未来方向, 包括异常检测的可解释性、征兆事件的动态表征与定位、趋势预测与因果推理的融合, 以及大模型应用潜力等.
- 航空器监测与检测 /
- 飞行状态评估 /
- 数据驱动 /
- 异常检测 /
- 征兆挖掘 /
- 实时飞行监控系统
Abstract: As global flight volume and operational density rise, traditional manual monitoring methods are gradually revealing limitations in coping with complex and dynamic flight environments. Recently, data-driven intelligent monitoring methods have become a research hotspot for improving flight safety and operational efficiency. This paper reviews flight safety state assessment methods based on large-scale time-series data. Based on the temporal characteristics of flight data, three key assessment methods across different temporal dimensions are summarized: Anomaly detection, precursor mining, and trend tracking and prediction, covering flight anomalous state identification, early warning, and prediction of potential risk events. First, these methods are systematically defined, covering “past-present-future” safety assurance. Next, representative research progress and existing limitations of various methods are introduced; Using high-risk approach phases as a representative scene, this study analyzes the application status and coordination mechanisms of assessment methods and reviews supporting technologies, including data communication and software platforms. Finally, current challenges and future directions are summarized, including interpretability in anomaly detection, dynamic representation and localization of precursor events, integration of trend prediction with causal inference, and potential applications of large language models.
- aircraft monitoring and detection /
- flight state assessment /
- data-driven /
- anomaly detection /
- precursor mining /
- real-time flight monitoring system

HTML全文

图 1 航空安全保障系统项目发展脉络

Fig. 1 Development trajectory of aviation safety assurance system projects

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图 2 即时航空安全管理系统架构

Fig. 2 Structure of the in-time aviation safety management system

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图 3 飞行安全状态评估方法关系

Fig. 3 Relationship among flight safety state assessment methods

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图 4 异常检测方法分类

Fig. 4 Classification of anomaly detection methods

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图 5 基于学习机制的征兆挖掘方法分类

Fig. 5 Classification of learning mechanism-based precursor mining methods

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图 6 趋势跟踪与预测方法分类

Fig. 6 Classification of trend tracking and prediction methods

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图 7 飞机一般进近程序示意图

Fig. 7 Schematic diagram of the general aircraft approach procedure

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图 8 征兆与异常事件的关系示意图

Fig. 8 Schematic diagram of correlation between precursors and anomalous events

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图 9 飞机机载实时监控系统示意图

Fig. 9 Schematic diagram of aircraft on-board real-time monitoring system

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表 1 三类飞行安全状态评估方法的功能与技术特征对比

Table 1 Functional and technical characteristic comparison of three flight safety state assessment methods

属性维度	异常检测方法	征兆挖掘方法	趋势跟踪与预测方法
评估目标	当前异常飞行状态识别	历史数据高风险征兆定位	未来飞行状态演化推演
数据依赖	当前/近期数据	历史数据、先验知识	当前/近期数据、异常/征兆状态
典型输出	异常区间、异常分数	征兆表征、因果关联特征	趋势曲线、未来状态预测
关键特征	显性异常	隐性异常、风险关联	状态趋势
典型应用环节	在线监测、快速告警	历史事故原因分析、提前预警与干预	状态趋势监控、风险评估、辅助决策

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表 2 飞行数据常见变量汇总

Table 2 Summary of general variables of flight data

类型	变量名
连续变量	速度/加速度	空速
		马赫数
		三轴加速度
		低引擎速度
		高引擎速度
	姿态角	俯仰角
		滚转角
		偏航角
	操纵面变量	副翼位置
		升降舵位置
		方向舵位置
	高度	气压高度
	高度	无线电高度
	气流角	迎角
	气流角	侧滑角
	环境变量	风速矢量
		温度
		气压
	飞行其他状态量
离散变量	部件/飞行状态	襟翼状态
		减速板状态
		起落架状态
		刹车状态
		反推状态
		飞行阶段(起飞、爬升、巡航、下降、进近、着陆)
	飞行控制模式	自动驾驶模式
		飞机指引模式
		进近模式
		自动油门模式
	飞机告警状态	状态超限
	飞机告警状态	故障/失效状态

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