基于振动传感器信号的短时间里埃转换和改进的加权剩余网络的CNC轴的旋转错误预测
1School of Intelligent Manufacturing, Panzhihua University, Panzhihua 617000, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
这项研究引入了一个自适应加权深度残余网络 (ResNet) 来预测CNC设备中的轴旋转错误. 该模型准确地将振动数据映射到旋转误差,改善加工质量预测.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 轴旋转误差是CNC设备性能和工件加工质量的关键指标.
- 当前的预测方法往往忽略了不同传感器数据输入的不同重要性.
研究的目的:
- 开发一种先进的深度学习模型,用于准确预测轴旋转错误.
- 为了在轻松获取的振动数据和复杂的旋转错误之间建立可靠的映射.
主要方法:
- 利用多传感器振动数据,使用短时间里埃变换 (STFT) 进行处理,以提取特征.
- 开发了一个自适应加权深度残余网络 (AWResNet),结合了用于特征重新校准的注意力机制.
- 员工堆叠剩余块和注意力单元以适应权重数据通道,强调关键信息并减少冗余.
主要成果:
- 适应加权ResNet (AWResNet) 证明了学习通道重新校准权重的能力.
- 与其他评估的深度学习模型相比,AWResNet实现了更高的预测准确性.
- 重量可视化证实了模型适应性通道权重的能力.
结论:
- 开发的AWResNet模型有效地预测了CNC设备中的轴旋转错误.
- 这种方法通过自适应地利用传感器数据,比现有方法提供了显著的改进.
- 该模型为增强CNC性能监测和加工质量评估提供了可靠的工具.
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