实现主要组件分析 (PCA) /单值分解 (SVD) 和神经网络在构建一个减少顺序模型的机械应力虚拟传感
M A Melgarejo1, A Pérez1, D Ruiz1
1Fundación Centro Tecnológico CTC-Scientific and Technological Park of Cantabria (PCTCAN), Street Isabel Torres Nº 1, 39011 Santander, Spain.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
本研究介绍了一种人工智能驱动的虚拟压力传感器,使用减少顺序模型 (ROM) 进行实时机械压力监测. 与传统模拟相比,该方法显著加快了分析速度,从而实现了高效的预测性维护.
科学领域:
- 计算力学是计算力学.
- 在工程领域的人工智能.
- 预测性维护是一种预测性维护.
背景情况:
- 高分辨率的有限元法 (FEM) 模拟是实时压力监测的计算密集型.
- 现有的方法在各种负载条件下对机械部件进行快速分析是困难的.
研究的目的:
- 开发一种人工智能驱动的虚拟压力感应方法,使用减少顺序模型 (ROM).
- 为了实时监控元件中的机械应力.
- 为了降低计算复杂性,同时保持高精度.
主要方法:
- 使用在FEM模拟输出上训练的神经网络构建ROM.
- 实现ROM作为虚拟应力传感器进行实时分析.
- 验证ROM对FEM的准确性和效率.
主要成果:
- 人工智能驱动的ROM实现了压力预测的平均绝对误差 (MAE) 为 (0.04±0.06) MPa.
- 虚拟传感器在0.13秒内生成了完整的应力图,比FEM的6分31秒显著减少.
- 该方法证明了实时压力监测的高精度和效率.
结论:
- 由人工智能驱动的ROM框架为虚拟应力传感提供了计算效率高,准确的解决方案.
- 这种方法可以实现有效的实时压力监测和详细的压力映射.
- 该方法具有通过压力周期监测推进预测性维护策略的巨大潜力.
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