基于数字双胞胎的技术研究包括全面的轮故障诊断和结构性能评估
Qiang Zhang1, Zhe Wu2, Boshuo An2
1Key Laboratory of Vehicle Transmission, China North Vehicle Research Institute, Beijing 100072, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
本研究介绍了一种使用数字双胞胎和人工智能的集成变速箱监控系统,用于实时故障诊断和性能预测. 该系统实现了超过99%的故障诊断准确度,提高了工业设备的预测性维护.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 数字双胞胎技术的数字双胞胎技术
背景情况:
- 现代工业变速箱是关键的传动部件,但当前的监控方法存在数据利用不足和评估不完整的问题.
- 现有的系统缺乏全面的实时故障诊断和性能预测能力,阻碍了有效的预测性维护.
- 挑战包括有限的监控,单一检测指数,以及当前变速箱运行评估中的低数据利用率.
研究的目的:
- 开发一个集成的变速箱监控系统,利用数字双胞胎和人工智能进行增强的操作评估.
- 通过虚拟现实映射实现实时故障诊断,性能预测和轮状况动态可视化.
- 为工业设备中先进的预测性维护应用奠定基础.
主要方法:
- 为数字双胞胎系统开发了五层架构:功能服务,软件支持,模型集成,数据驱动和数字双胞胎层.
- 利用HyperMesh和ABAQUS进行精细网格生成和热流体固体合模拟,以分析轮应力分布.
- 实施了高斯过程回归 (GPR) 模型用于应力预测,以及结合波形变换和深度残留收缩网络 (DRSN) 的故障诊断算法.
主要成果:
- 综合系统在各种故障类型 (破牙,磨损,插孔) 的故障诊断中实现了超过99%的准确性.
- 应力预测模型表现出高精度,驱动轮的平均R2为0.9339,驱动轮的R2为0.9497.
- 该系统成功地支持实时显示3D云图像,可视化轮应力分布.
结论:
- 拟议的数字双胞胎和基于人工智能的系统为智能工业设备监控提供了一种新有效的方法.
- 该系统的多源数据融合,交互和可视化功能显著提升了预测性维护.
- 未来的工作重点应该是提高有限元模拟精度,解决获得实际应力数据的挑战.
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