认知数字孪生赋能航空装备：新范式与研究前沿

doi:10.3901/JME.260533

机械工程学报 ›› 2026, Vol. 62 ›› Issue (12): 1-21.doi: 10.3901/JME.260533

• 特邀专栏：数字孪生赋能的高端装备智能运维 • 上一篇

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认知数字孪生赋能航空装备：新范式与研究前沿

冯珂¹, 张紫琳¹, 倪清², 杨彬³, 胡亚安⁴, 孟理华⁵, 雷亚国¹

1. 西安交通大学精密微纳制造技术全国重点实验室西安 710054;
2. 西北工业大学光电与智能研究院西安 710072;
3. 西安交通大学现代设计及转子轴承系统教育部实验室西安 710049;
4. 水利部交通运输部国家能源局南京水利科学研究院南京 210029;
5. 中国航空综合技术研究所北京 100028

收稿日期:2025-10-08 修回日期:2025-12-30 发布日期:2026-08-03
作者简介:冯珂，男，1992年出生，教授，博士研究生导师。主要研究方向为数字孪生、智能运维、人工智能、信号处理、动力学等。E-mail:kefeng@xjtu.edu.cn
雷亚国(通信作者)，男，1979年出生，教授。主要研究方向为高端装备智能运维、机械动力学、信号处理等。E-mail:yaguolei@mail.xjtu.edu.cn
基金资助:
国家自然科学基金(52575137)和新基石科学基金会“科学探索奖”(XPLORER-2024-1036)。

Cognitive Digital Twin Enabled Aerospace Equipment: New Paradigm and Research Frontier

FENG Ke¹, ZHANG Zilin¹, NI Qing², YANG Bin³, HU Yaan⁴, MENG Lihua⁵, LEI Yaguo¹

1. State Key Laboratory for Manufacturing System Engineering, Xi'an Jiaotong University, Xi'an 710054;
2. School of Artificial Intelligence, OPtics and ElectroNics, Northwestern Polytechnical University, Xi'an 710072;
3. Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an 710049;
4. Nanjing Hydraulic Research Institute, Nanjing 210029;
5. China Aero-Polytechnology Establishment, Beijing 100028

Received:2025-10-08 Revised:2025-12-30 Published:2026-08-03

摘要/Abstract

摘要： 航空装备作为现代军事与民用航空领域的核心设施，其系统高度复杂、服役周期长、可靠性要求严苛，对装备的研制与运维提出了严峻挑战。数字孪生技术为航空装备全生命周期管理提供了有效支撑，但传统数字孪生仍停留在静态表征层面，难以实现对装备复杂动态行为的深度认知、演化预测及干预决策。在此背景下，随着大模型、知识图谱与因果推理等认知技术兴起，认知数字孪生成为新一代的研究焦点。当前，认知数字孪生的成果多局限于局部特定场景，其理论体系亟需完善，缺乏面向航空装备复杂系统的统一架构。鉴于此，梳理了一种面向航空装备全生命周期管理的新型认知数字孪生框架，旨在厘清从状态映射到自主认知的范式跃迁路径。首先，归纳了数字孪生技术从虚实映射向智能认知演进的发展脉络，并分析了该演进趋势在航空领域的应用现状与迫切需求。其次，重点分析了由物理实体层、虚拟实体层、交互连接层与认知嵌入层构成的认知数字孪生框架，及其在全生命周期的认知赋能机制。随后，从实现方法角度归纳了各层级的研究现状和技术路径。针对当前航空装备数字孪生应用面临的关键挑战，结合大模型等新兴技术，展望了以深度理解、自主演化与可信决策为核心特征的认知数字孪生体系发展图景。

关键词: 航空装备, 认知数字孪生, 全生命周期, 自主演化, 智能决策

Abstract: As core assets in modern military and civil aviation, aviation equipment features high complexity, long service lifecycles, and stringent reliability requirements, posing severe challenges to equipment development and operation maintenance. Digital twin technology provides effective support for the full-lifecycle management of aviation equipment. However, conventional digital twin technologies remain confined to static representation, making them insufficient to support deep cognition, evolutionary prediction, and intervention-oriented decision-making for the complex and dynamic behaviors of equipment throughout its lifecycle. In this context, driven by rapid advances in cognitive intelligence technologies such as large models, knowledge graphs, and causal reasoning, cognitive digital twins are emerging as a new research frontier. Existing studies on cognitive digital twins are largely limited to localized explorations within specific scenarios. Their theoretical system remains underdeveloped, and a unified architecture for complex aviation systems is lacking. In view of these gaps, a novel cognitive digital twin framework for the full-lifecycle management of aviation equipment is proposed, aiming to elucidate the paradigm shift from state mapping to autonomous cognition. First, it reviews the evolution of digital twin technology from “virtual-physical mapping” to “intelligent cognition”, and analyzes the current applications and urgent needs of this transition in the aviation domain. Second, the cognitive digital twin framework is deconstructed into its constituent layers—physical entity, virtual entity, interactive connection, and cognitive embedding. It also provides a detailed explanation of the cognition-enabling mechanisms of cognitive digital twins across the full lifecycle of the equipment. Subsequently, it systematically summarizes the research status and technical pathways of each layer from an implementation perspective. Finally, in response to current challenges faced by the digital twin application of aviation equipment, it explores the integration of emerging technologies including large models, and looks ahead to future trends in constructing a cognitive digital twin system for aviation equipment with deep comprehension, autonomous evolution, and trustworthy decision-making capabilities.

Key words: aerospace equipment, cognitive digital twin, full lifecycle, autonomous evolution, intelligent decision-making

中图分类号:

TG156

冯珂, 张紫琳, 倪清, 杨彬, 胡亚安, 孟理华, 雷亚国. 认知数字孪生赋能航空装备：新范式与研究前沿[J]. 机械工程学报, 2026, 62(12): 1-21.

FENG Ke, ZHANG Zilin, NI Qing, YANG Bin, HU Yaan, MENG Lihua, LEI Yaguo. Cognitive Digital Twin Enabled Aerospace Equipment: New Paradigm and Research Frontier[J]. Journal of Mechanical Engineering, 2026, 62(12): 1-21.

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http://www.cjmenet.com.cn/CN/Y2026/V62/I12/1

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认知数字孪生赋能航空装备：新范式与研究前沿

Cognitive Digital Twin Enabled Aerospace Equipment: New Paradigm and Research Frontier

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