路头轴承的故障诊断方法基于VMD和域自适应转移学习
1School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一种新的方法,用于通过变化模式分解和域自适应卷积神经网络来诊断路头轴承故障. 该技术可以在复杂的采矿环境中准确地识别早期轴承故障.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 路头轴承对于安全的地下采矿至关重要,但在恶劣条件下容易发生故障.
- 早期轴承故障表现出微妙的振动特征,被显著的操作噪声掩盖.
- 现有的诊断方法在不同的工作条件下难以应对振动数据的变化.
研究的目的:
- 为路头轴承制定一个有效的故障诊断策略.
- 为了应对噪音环境中弱故障信号的挑战.
- 在可变的操作条件下提高诊断准确度.
主要方法:
- 变化模式分解 (VMD) 用于将振动信号分解为内在模式函数 (IMF).
- 在IMF上计算Kurtosis指数以提取与故障相关的特征.
- 域自适应卷积神经网络 (CNN) 具有深度转移学习用于分类.
主要成果:
- 拟议的VMD-CNN方法成功分解了振动信号,并提取了关键特征.
- 深度转移学习有效地处理了不同工作条件的数据分布变化.
- 该策略在现实世界的路头轴承故障诊断中表现出卓越的诊断准确性.
结论:
- 结合VMD和域自适应CNN方法,为路头轴承故障诊断提供了强大的解决方案.
- 这种方法提高了地下采矿操作的可靠性和安全性.
- 该技术显示出显著的实际工程应用价值.
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