基于Frobenius和核混合规范的对称点图案提取方法惩罚了强大的主要组件分析和分解和重建
Lijing Wang1, Shichun Wei1, Tao Xi2
1School Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究引入了一种新的方法,可以从杂的滚动轴承振动信号中提取对称点图案 (SDP). 该技术通过强大的主要组件分析和信号分解有效地消除信号,提高故障诊断的准确性.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 振动分析 振动分析
背景情况:
- 滚动轴承易受噪声的影响,使使用振动分析复杂化故障检测.
- 传统方法与强烈的干扰作斗争,影响了特征提取技术的可靠性,例如对称点图案 (SDP).
研究的目的:
- 开发一种先进的方法,从滚动轴承的噪音振动信号中提取强大的对称点图案 (SDP).
- 通过在特征提取之前有效地消除信号来提高故障诊断的准确性.
主要方法:
- 弗罗贝尼乌斯和核混合规范对强大的主要组件分析 (FNHN-RPCA) 进行了惩罚,以消除信号和特征分离.
- 集体实证模式分解 (EEMD) 将信号分解为内在模式函数 (IMF).
- 改进了DPR/KLdiv标准,用于选择包含重建故障特征的相关IMF.
- 对称点图案 (SDP) 是对重建信号的可视化.
主要成果:
- FNHN-RPCA有效地将故障特征与干扰噪声分开.
- 改进的DPR/KLdiv标准准确地选了包含故障信息的组件.
- 拟议的方法显著减少了强噪声对SDPs的影响.
- 在CWRU数据集和实验数据上的验证证实了该方法的有效性.
结论:
- 开发的FNHN-RPCA和基于EMD的方法提供了一种强大的方法,用于从噪音大的轴承振动数据中提取SDP.
- 这种技术提高了在具有挑战性的噪音条件下滚动轴承故障诊断的可靠性.
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