通过多目标优化减轻建筑机械数字双胞胎中的测量不准确性
Misganaw Abebe1, Yonggeun Cho2, Seung Chul Han2
1Department of Mechanical Engineering, Kunsan National University, Gunsan 54150, Republic of Korea.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究介绍了一种新的数字双胞胎优化策略,使用高斯过程回归和遗传算法. 它有效地减少了不确定性,将虚拟模型与现实世界的性能对齐,以改进系统设计.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 优化优化 优化优化
背景情况:
- 数字双胞胎需要精确的数据来准确地复制系统.
- 传感器和模型的不确定性会在数字和物理系统之间产生差异.
- 缓解这些不确定性对于可靠的数字双胞胎应用程序至关重要.
研究的目的:
- 为数字双胞胎开发一个原创的多目标优化策略.
- 将不确定的参数集成到高斯过程回归替代模型中.
- 增强数字双胞胎模型与实际系统性能之间的调整.
主要方法:
- 用高斯过程回归来替代模型.
- 集成的不确定参数:负载角度,桶,臂和弹杆气的冲动.
- 采用遗传算法来识别气压力的帕雷托边界.
- 应用TOPSIS (通过理想解决方案的相似性排序优先级的技术) 来选择最佳解决方案.
主要成果:
- 拟议的多目标优化战略有效地整合了不确定的参数.
- 确定了弹杆,臂和桶气压力的帕雷托边界.
- 托普西斯成功地从帕雷托集中确定了一个最佳参数配置.
- 在虚拟模型的实验和数值结果之间证明了实质性的一致性.
结论:
- 开发的数字双胞胎优化策略成功地减轻了不确定性.
- 高斯过程回归,遗传算法和TOPSIS的整合提供了一个强大的方法.
- 这些发现验证了系统设计和分析的优化数字双胞胎模型的准确性和可靠性.
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