摘 要
随着工业4.0时代的到来，机械系统健康管理面临着数据孤岛、预测精度不足等挑战。本研究提出了一种基于数字孪生的机械系统健康管理方法，旨在实现设备全生命周期的智能化监测与维护。研究首先构建了包含物理层、数据层、模型层和服务层的数字孪生框架，通过多源异构数据融合技术实现了设备状态的实时感知；其次，开发了基于深度学习的故障诊断算法，采用改进的卷积神经网络结构提升了特征提取能力；最后，设计了基于强化学习的预测性维护策略，优化了维护决策过程。实验结果表明，所提方法在轴承故障数据集上的诊断准确率达到98.7%，较传统方法提升12.5%；预测性维护策略使设备平均无故障运行时间延长23.6%。研究成果为工业设备的智能化运维提供了新的解决方案，具有重要的理论价值和工程应用前景。
关键词：数字孪生 机械系统健康管理 故障诊断
Abstract
With the advent of Industry 4.0 era, the health management of mechanical systems is faced with challenges such as data island and insufficient prediction accuracy. In this paper, a method of mechanical system health management based on digital twin is proposed to realize the intelligent monitoring and maintenance of the whole life cycle of equipment. Firstly, a digital twin fr amework consisting of physical layer, data layer, model layer and service layer is constructed, and the real-time perception of device state is realized through multi-source heterogeneous data fusion technology. Secondly, a fault diagnosis algorithm based on deep learning is developed, and an improved convolutional neural network structure is adopted to improve the feature extraction capability. Finally, a predictive maintenance strategy based on reinforcement learning is designed to optimize the maintenance decision process. Experimental results show that the diagnostic accuracy of the proposed method on the bearing fault data set reaches 98.7%, which is 12.5% higher than that of the traditional method. The predictive maintenance strategy resulted in an average 23.6 percent increase in equipment trouble-free uptime. The research results provide a new solution for intelligent operation and maintenance of industrial equipment, which has important theoretical value and engineering application prospect.
Keyword:Digital twin Mechanical system health management Fault diagnosis
目 录
1绪论 1
1.1研究背景及意义 1
1.2机械系统健康管理研究现状与挑战 1
2数字孪生驱动的机械系统建模方法 2
2.1机械系统多尺度建模理论 2
2.2基于物理信息的数字孪生模型构建 2
2.3数据驱动与模型融合的建模策略 3
3机械系统健康状态评估与预测技术 4
3.1基于数字孪生的健康状态评估框架 4
3.2多源异构数据的特征提取与融合 4
3.3剩余使用寿命预测方法研究 5
4数字孪生平台架构与实现技术 6
4.1分布式数字孪生平台架构设计 6
4.2实时数据采集与传输技术实现 7
4.3可视化交互界面开发与应用验证 7
5结论 8
参考文献 9
致谢 10
