交叉转速螺杆电机轴承故障诊断结合多空间变量尺度自适应过器和前混合战略
Hao Zhou1, Jianzhong Yang1, Qian Zhu1
1National Numerical Control System Engineering Research Center, Huazhong University of Science and Technology, Mechanical Building, 1037 Luoyu Road, Wuhan 430074, China.
ISA transactions
|December 8, 2024
概括
本研究介绍了一种自适应性正弦融合卷积神经网络 (ASFCNN),用于在不同速度的CNC机器中诊断螺旋电机轴承故障. ASFCNN有效降低噪音,并增强故障特征,以提高准确性.
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:
- 在CNC加工中,线轴电机的振动信号是复杂的,含有噪声和调制波.
- 由于信号特征的变化,螺旋转速的变化使故障诊断复杂化.
研究的目的:
- 开发一个强大的故障诊断方法,用于轴承螺旋电机,有效地运行在不同的速度.
- 为应对CNC机床诊断中噪声和信号变化所带来的挑战.
主要方法:
- 提出了一个自适应性鼻状融合卷积神经网络 (ASFCNN) 模型.
- 使用多空间可变尺度自适应性阴影过器 (MVASF) 来降低噪音和提取特征.
- 实施了多层次的前混合战略 (MFHS),以融合CNN的功能和时间序列信息.
主要成果:
- 在现实数据集上,ASFCNN模型显示出与经典方法相比,更高的诊断准确性.
- 实现了有效的降噪和故障特征增强.
- 使用可视化技术验证了模型的有效性和可解释性.
结论:
- 拟议的ASFCNN方法为螺旋电机轴承的横速故障诊断提供了显著的进步.
- MVASF和MFHS的集成提供了一个强大的方法来处理复杂的振动信号.
- 该研究验证了ASFCNN在CNC机床中可靠和可解释的故障诊断能力.
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