滚动轴承的新故障特征提取方法基于改进的自我选择ICEEMDAN-变
Maohua Xiao1, Zhenyu Wang1, Yuanfang Zhao1
1College of Engineering, Nanjing Agricultural University, Nanjing 210031, China.
ISA transactions
|September 28, 2023
概括
这项研究引入了一种改进的ICEEMDAN方法,用于滚动轴承故障诊断. 新技术提高了特征提取的准确性,在实验中达到99.5%的诊断准确性.
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是复杂的,使得特征提取具有挑战性.
- 现有的实证模式分解 (EMD) 方法存在模式别名问题.
研究的目的:
- 提出一种用于滚动轴承故障诊断的新型特征提取方法.
- 提高故障分类的准确性和智能性.
主要方法:
- 开发了一种改进的完整合体实证模式分解与适应噪声 (ICEEMDAN) 算法,具有自选功能.
- 改进的ICEEMDAN将振动信号分解成内在模式功能.
- 从这些函数中提取了变作为故障特征.
主要成果:
- 拟议的方法有效地分离了不同状态下的轴承的振动信号特征.
- 对Case Western Reserve大学数据集的验证证实了它的有效性.
- 实验结果显示诊断准确率为99.5%,比未改进的ICEEMDAN增加6.4%.
结论:
- 开发的基于ICEEMDAN的 permutation entropy方法为滚动轴承故障诊断提供了有效和智能的特征提取.
- 与传统方法相比,这种方法显著提高了诊断准确度.
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