系统仿真学报 ›› 2026, Vol. 38 ›› Issue (4): 1004-1017.doi: 10.16182/j.issn1004731x.joss.25-0854

• 论文 • 上一篇    下一篇

数字孪生驱动的核电连接套质检设备状态监测

南焱栋1, 朱金达1, 路鑫彬1, 秦志英1, 齐丹丹2, 丁智恒1   

  1. 1.河北科技大学 机械工程学院,河北 石家庄 050018
    2.河北化工医药职业技术学院,河北 石家庄 050026
  • 收稿日期:2025-09-04 修回日期:2025-11-07 出版日期:2026-04-20 发布日期:2026-04-22
  • 通讯作者: 朱金达
  • 第一作者简介:南焱栋(2000-),男,硕士生,研究方向为复杂装备数字孪生技术。
  • 基金资助:
    中央引导地方科技发展资金(254Z1802G);河北化工医药职业技术学院智能机电应用技术协同创新中心开放基金(ZXJJ202504001)

State Monitoring of Nuclear Power Connection Sleeve Quality Inspection Equipment Driven by Digital Twin

Nan Yandong1, Zhu Jinda1, Lu Xinbin1, Qin Zhiying1, Qi Dandan2, Ding Zhiheng1   

  1. 1.College of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
    2.Hebei Chemical & Pharmaceutical College, Shijiazhuang 050026, China
  • Received:2025-09-04 Revised:2025-11-07 Online:2026-04-20 Published:2026-04-22
  • Contact: Zhu Jinda

摘要:

针对核电连接套质检设备存在状态感知滞后、监测维度单一及可视化效果不足的问题,提出了一种数字孪生驱动的核电连接套质检设备状态监测方法。构建了质检设备的数字孪生状态协同监测框架,基于OPC UA技术建立多源信息互联模型。通过有限状态机模型对质检流程进行离散化逻辑驱动,并提出层次化验证策略,建立多维度运动状态监测机制;构建了径向基插值函数与高斯过程回归耦合的代理模型。实验结果表明:该方法实现了运动状态异常平均检测精度达98%、报警响应时间小于0.5 s。在保证力学数据保真度的同时将计算时间缩短至0.15 s,应力预测R2达0.92,应变预测R2达0.81,平衡了复杂工况下力学状态监测的精度与效率,为核电安全壳钢筋连接套的“全检测、全合格”要求提供了有效解决方案。

关键词: 数字孪生, 代理模型, 有限状态机模型, 状态监测, 核电连接套质检设备

Abstract:

To address the problems of delayed state perception, single monitoring dimension, and insufficient visualization in the quality inspection equipment for nuclear power connection sleeves, a state monitoring method driven by digital twin was proposed. A digital twin-based collaborative state monitoring framework for the inspection equipment was constructed. Based on the OPC UA technology, a multi-source information interconnection model was established. A finite state machine model was employed to discretize and logically drive the inspection process, and a hierarchical verification strategy was proposed to establish a multi-dimensional motion state monitoring mechanism. A surrogate model coupling the radial basis interpolation function and Gaussian process regression was constructed. The experimental results indicate that the proposed method achieves an average motion state anomaly detection accuracy of 98% and an alarm response time of less than 0.5 s. While preserving the fidelity of mechanical data, the computation time is reduced to 0.15 s, achieving an R2 of 0.92 for stress prediction and 0.81 for strain prediction, effectively balancing the accuracy and efficiency of mechanical state monitoring under complex working conditions. This method provides an effective solution to meet the "full inspection and full qualification" requirements for nuclear power containment rebar connection sleeves.

Key words: digital twin, surrogate model, finite state machine model, state monitoring, nuclear power connection sleeve quality inspection equipment

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