用机器学习方法对球轴承的辐射内部空隙进行智能诊断
Bartłomiej Ambrożkiewicz1,2, Arkadiusz Syta3, Anthimos Georgiadis2
1Department of Automation, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究使用振动分析对滚动轴承的内部空隙进行了分类. 变化模式分解 (VMD) 显著提高了分类准确度,达到90%以上.
科学领域:
- 机械工程 机械工程
- 振动分析 振动分析
- 机器学习 机器学习
背景情况:
- 滚动轴承的半径内部空隙很难通过动态分析来检测.
- 现有的轴承距离分类方法缺乏足够的准确性.
- 智能检测轴承空隙对于预测性维护至关重要.
研究的目的:
- 开发一种智能方法来分类滚动轴承的辐射内部空隙.
- 用先进的信号处理来提高轴承空隙检测的准确性.
- 为了评估变化模式分解 (VMD) 在轴承诊断中的有效性.
主要方法:
- 分析来自滚动轴承的短时间振动数据.
- 用于特征提取的统计指标的计算.
- 机器学习模型用于分类的应用.
- 使用变化模式分解 (VMD) 进行增强的时间序列处理.
主要成果:
- 使用统计指标的初始分类准确性不令人满意.
- VMD成功地将主导频率提取到实验模式.
- 将统计指标应用于VMD模式,将分类准确度提高到90%以上.
- 该研究证明了VMD在分类轴承内部清除方面的有效性.
结论:
- 与统计指标相结合的VMD提供了一个非常准确的方法来分类滚动轴承的辐射内部空隙.
- 这种方法提高了滚动元件轴承的诊断能力.
- 这些发现有助于更可靠的状态监测和预测性维护策略.
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