Zhang Qiang, Wu Zhe, An Boshuo, Sun Ruitian, Cui Yanping
Key Laboratory of Vehicle Transmission, China North Vehicle Research Institute, Beijing 100072, China.
School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.
Sensors (Basel). 2025 Apr 27;25(9):2775. doi: 10.3390/s25092775.
In the operation process of modern industrial equipment, as the core transmission component, the operation state of the gearbox directly affects the overall performance and service life of the equipment. However, the current gear operation is still faced with problems such as poor monitoring, a single detection index, and low data utilization, which lead to incomplete evaluation results. In view of these challenges, this paper proposes a shape and property integrated gearbox monitoring system based on digital twin technology and artificial intelligence, which aims to realize real-time fault diagnosis, performance prediction, and the dynamic visualization of gear through virtual real mapping and data interaction, and lays the foundation for the follow-up predictive maintenance application. Taking the QPZZ-ii gearbox test bed as the physical entity, the research establishes a five-layer architecture: functional service layer, software support layer, model integration layer, data-driven layer, and digital twin layer, forming a closed-loop feedback mechanism. In terms of technical implementation, combined with HyperMesh 2023 refinement mesh generation, ABAQUS 2023 simulates the stress distribution of gear under thermal fluid solid coupling conditions, the Gaussian process regression (GPR) stress prediction model, and a fault diagnosis algorithm based on wavelet transform and the depth residual shrinkage network (DRSN), and analyzes the vibration signal and stress distribution of gear under normal, broken tooth, wear and pitting fault types. The experimental verification shows that the fault diagnosis accuracy of the system is more than 99%, the average value of the determination coefficient (R) of the stress prediction model is 0.9339 (driving wheel) and 0.9497 (driven wheel), and supports the real-time display of three-dimensional cloud images. The advantage of the research lies in the interaction and visualization of fusion of multi-source data, but it is limited to the accuracy of finite element simulation and the difficulty of obtaining actual stress data. This achievement provides a new method for intelligent monitoring of industrial equipment and effectively promotes the application of digital twin technology in the field of predictive maintenance.
在现代工业设备的运行过程中，作为核心传动部件，齿轮箱的运行状态直接影响设备的整体性能和使用寿命。然而，当前齿轮运行仍面临监测手段不足、检测指标单一、数据利用率低等问题，导致评估结果不完整。针对这些挑战，本文提出了一种基于数字孪生技术和人工智能的形状与特性集成齿轮箱监测系统，旨在通过虚实映射和数据交互实现齿轮故障实时诊断、性能预测及动态可视化，为后续的预测性维护应用奠定基础。以QPZZ-ii齿轮箱试验台为物理实体，研究建立了五层架构：功能服务层、软件支撑层、模型集成层、数据驱动层和数字孪生层，形成闭环反馈机制。在技术实现方面，结合HyperMesh 2023进行细化网格生成，利用ABAQUS 2023模拟热流固耦合条件下齿轮的应力分布，建立高斯过程回归（GPR）应力预测模型以及基于小波变换和深度残差收缩网络（DRSN）的故障诊断算法，并分析了正常、断齿、磨损和点蚀故障类型下齿轮的振动信号和应力分布。实验验证表明，该系统的故障诊断准确率超过99%，应力预测模型的决定系数（R）平均值分别为0.9339（主动轮）和0.9497（从动轮），并支持三维云图实时显示。该研究的优势在于多源数据融合的交互与可视化，但受限于有限元模拟精度及实际应力数据获取难度。该成果为工业设备智能监测提供了新方法，有效推动了数字孪生技术在预测性维护领域的应用。
