基于层级数字孪生的机电系统故障数据生成
收稿日期: 2025-08-06
修回日期: 2025-08-27
录用日期: 2025-10-22
网络出版日期: 2025-11-25
基金资助
科技创新2030(2021ZD0201300)
Fault data generation for electromechanical systems based on hierarchical Digital Twin
Received date: 2025-08-06
Revised date: 2025-08-27
Accepted date: 2025-10-22
Online published: 2025-11-25
Supported by
STI 2030—Major Projects(2021ZD0201300)
多耦合机电系统（MES）是航空装备的关键组成部分，其稳定运行高度依赖于高效的故障诊断。随着人工智能技术的发展，充足的故障数据对提升MES故障诊断性能至关重要。然而，实际应用中获取故障数据极为困难。因此，亟需通过系统仿真生成虚拟故障数据以提升诊断能力。数字孪生技术凭借其卓越的实体特征虚拟映射能力，为数据生成提供了潜在途径。但目前仍缺乏有效方法来解耦MES实体并构建其全系统故障数字孪生模型。针对此目标，提出一种层级数字孪生建模方法以实现故障数据生成。为结构化描述复杂实体，首先将MES解耦为包含要素、关系和数据的三元组表示，涵盖多维度与多模态信息。基于要素和数据表示，提出层级数据-模型融合技术，构建空间、行为、过程和状态四级数字孪生子模型，在MES局部虚拟化过程中有效平衡建模适应度与精度。进而，这些异构数字孪生子模型依据关系表示，通过协同编排算法交互并集成为全系统数字孪生，共同构成MES实体在各类故障模式下的全局镜像。在多耦合机电故障试验台上验证了所提方法的有效性。结果表明，该方法将故障诊断准确率平均提升了11.95%，验证了其在生成MES等复杂实体故障数据方面的优越性。
丁宇 , 宁国澳 , 靳凯新 , 孙博 , 李淮 , 苏铉元 . 基于层级数字孪生的机电系统故障数据生成[J]. 航空学报, 2026 , 47(8) : 432663 -432663 . DOI: 10.7527/S1000-6893.2025.32663
Multi-coupled Electromechanical Systems （MES） are the important component of modern industry， their stable operation heavily relies on effective fault diagnosis. With the development of artificial intelligence technique， sufficient fault data plays an important role in MES’s fault diagnosis， while is quite difficult to obtain in practices. In this regard， there is urgent need to generate the virtual fault data through the system simulation and improve the fault diagnosis performance， where the Digital Twin technique with superior virtual mapping capabilities for entity characteristics provides the potential opportunity. However， there is a lack of an effective method that can decouple the complicated MES entity and construct its full-system fault Digital Twin model. Aiming at the above target， we propose a hierarchical collaborative Digital Twin modeling method to implement the fault data generation. In order to structurally describe the complicated entity， MES is decoupled and identified as the multidimensional and multimodal triplet representations， namely， element， relationship， and data. Given the element and data， the hierarchical data-model combined technique is proposed to develop the four-level （space， behavior， process， and status） sub-Digital Twin models， to well balance the modeling adaptability and modeling precision during the local virtualization of MES. Thanks for the proposed collaborative orchestration algorithm， these heterogeneous sub-Digital Twin models are further interacted and integrated into the full-system Digital Twin according to the aforementioned relationship representation， which jointly constitutes the global mirror of the MES entity under various fault modes. We conducted the experiments to validate the proposed method by using a multi-coupled electromechanical fault test bench. The experimental results show that our method improved the accuracy of fault diagnosis by an average of 11.95%， which demonstrates its superiority in the fault data generation for the complicated entity such as MES.
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