使用特征工程和主要组件分析来监测基于Kullback-Leibler分歧的螺旋速度变化,使用高斯混合模型.
Yi-Cheng Huang1, Ching-Chen Hou1
1Department of Mechanical Engineering, National Chung Hsing University, Taichung 402, Taiwan.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了一种使用人工智能进行CNC机床实时监控的智能传感方法. 开发的特征工程,主要组件分析和高斯混合模型方法有效诊断制造参数变化.
科学领域:
- 制造业 工程 制造工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 制造业正在转向智能机械,将人工智能集成到计算机数控 (CNC) 机床中,以进行自我诊断和提高产品质量.
- 制造参数的实时监控对于检测偏差和确保工艺稳定至关重要.
研究的目的:
- 开发和验证一种用于实时监测和诊断数控机床制造参数变化的新方法.
- 实现智能传感能力,以改善加工过程控制和质量保证.
主要方法:
- 结合特征工程和主要组件分析 (FE-PCA) 与在线高斯混合模型 (GMM),利用Kullback-Leibler分歧 (KLD) 进行无监督学习.
- 利用加速仪设备的振动信号和螺杆电流传感器来获取数据.
- 应用FE-PCA-GMM/KLD方法来实时诊断在削过程中发生的轴速变化.
主要成果:
- 开发的无监督学习模型成功诊断了数控机床中轴速度的变化.
- 在诊断跨轴变化方面获得高F1分数:X为0.95,Y为0.88,Z为0.93.
- 实验验证证了该方法在检测制造参数变化的有效性.
结论:
- 已建立的FE-PCA-GMM/KLD方法为实时监控和制造过程参数变化的早期预警提供了强大的方法.
- 开发的智能传感技术适用于制造用于诊断加工状态的设备,增强制造智能.
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