基于WPE的滚动轴承的故障诊断通过波形分解和ELM
1College of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
这项研究引入了一种新的故障诊断方法,用于滚动轴承,使用波纹分解和加权变量 (WPE) 与极端学习机器 (ELM). 先进的WPE技术在分类正常和故障轴承信号方面取得了很高的准确性.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 滚动轴承的振动信号复杂且不平滑,这给传统故障诊断带来了挑战.
- 准确的故障诊断对于防止设备故障和确保操作安全至关重要.
研究的目的:
- 开发一个有效的故障诊断分类方法,用于滚动轴承.
- 为了解决处理复杂的振动信号的现有方法的局限性.
主要方法:
- 波段分解 ("db3") 用于将信号分解成四个层的近似 (CA) 和详细 (CD) 组件.
- 为这些组件计算了加权转换 (WPE),以创建特征向量.
- 具有优化的参数的极端学习机器 (ELM) 用于信号分类.
主要成果:
- 提出的基于WPE的方法实现了100%的训练精度和98.57%的测试精度,用于分类七种信号类型 (正常和六种故障状态).
- 该方法与传统的变 (PE) 相比,显示出更高的性能.
- 通过五倍交叉验证确定37个隐藏层节点,实现了最佳的ELM性能.
结论:
- WPE与波纹分解和ELM相结合,为滚动轴承故障诊断提供了强大而准确的方法.
- 该方法为正常和故障轴承信号的多重分类提供了有效的指导.
- 这些发现有助于推进机械系统的状态监测和预测性维护.
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