用于由数字双胞胎驱动的线性电机料系统的性能降解评估方法
Zeqing Yang1,2, Yiding Yao1, Wei Cui1
1School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China.
Scientific reports
|May 20, 2025
概括
本研究使用数字双子和格拉米安角场 (GAF) 与AlexNet准确评估线性电机养系统退化,克服预测性维护的数据限制.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 对于线性电机料系统的传统降解评估方法存在缺少样本和低准确度的问题.
- 数字双胞胎技术在复杂设备状态评估和性能预测方面提供了优势.
研究的目的:
- 引入数字双胞胎技术,以提高线性电机养系统的性能降解评估.
- 解决缺少降解样本的挑战,提高评估准确性.
主要方法:
- 建立了机械,电气和控制子系统的数字综合模型,分析了多场合机制.
- 格拉米安角场 (GAF) 编码将时间序列数据转换为图像,由AlexNet处理以进行降解评估.
- 模拟生成了正常和去磁降解条件的当前数据集.
主要成果:
- 拟议的GAF-AlexNet模型在评估不可逆转的去磁降解降解水平方面实现了98.3%的准确性.
- 与传统方法相比,AUC指数 (10.3-15.2%) 和平均精度 (10.5-14.7%) 显著改善.
- 该方法有效地解决了实际降解样本不足的问题.
结论:
- 数字双胞胎集成的GAF-AlexNet模型为线性电机料系统的性能降解评估提供了一个强大的工具.
- 这种方法增强了工业环境中的预测性维护和性能监测.
- 该研究成功地克服了传统方法的局限性和数据稀缺性.
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