There Search of LSSVM Based on LMD Morphology Filter

MENG Liang; XU Tong-le; MA Jin-ying; CAI Dao-yong

doi:10.15938/j.jhust.2022.01.012

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There Search of LSSVM Based on LMD Morphology Filter

更新时间：2026-08-31

- There Search of LSSVM Based on LMD Morphology Filter
- Vol. 27, Issue 1, Pages: 92-99(2022)
- 作者机构：
  
  1. 1山东理工大学机械工程学院,山东,淄博,255049
  2. 2山东理工大学农业工程与食品科学学院,山东,淄博,255049
  3. 3山东科大机电科技股份有限公司,山东,济宁,272000
- 作者简介：
- 基金信息：
- DOI：10.15938/j.jhust.2022.01.012
  CLC： TH707
- Published：2022
- 稿件说明：
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MENG Liang, XU Tong-le, MA Jin-ying, et al. There Search of LSSVM Based on LMD Morphology Filter[J]. 2022, 27(1): 92-99.
DOI：

MENG Liang, XU Tong-le, MA Jin-ying, et al. There Search of LSSVM Based on LMD Morphology Filter[J]. 2022, 27(1): 92-99. DOI： 10.15938/j.jhust.2022.01.012.

摘要

在轴承的故障诊断中，为了解决核函数在最小二乘支持向量机中参数选择困难及稀疏性差的问题，提出了局部均值分解（LMD）形态滤波的最小二乘支持向量机（LS-SVM）方法。该方法首先利用LMD对信号进行分解得到PF分量，并对信号做相关分析去除虚假分量，形态滤波降噪后再进行LMD分解得到新PF分量，提取能量特征；其次，对LS-SVM的核函数进行改进，解决核参数选择的问题；应用特征加权法对拉格朗日参数进行特征加权，取其加权平均值作为剪枝方法的阈值，降低稀疏性；最后将能量特征信号输入LS-SVM中，对信息进行训练预测。实验表明，应用该方法能快速有效地对轴承故障信号进行自适应的分类及轴承故障的判断。

Abstract

In the diagnosis of bearing

the LS-SVM method research with LMD morphological filtering was put out in order to solve the problem about the kernel function parameter selection and the bad sparsity of least squares vector machine (LS-SVM).First

the LMD was used to decompose the measured signal and PF components were obtained.The correlation analysis was carried out to remove the false components

and the noise of PF components was reduced by morphological filtering.The LMD decomposed the recombinational signal and obtained new PF components

and energy characteristics were got from the new PF component.Secondly

the kernel function of LS-SVM is improved to solve the problem of kernel parameter selection. Lagrange parameters were weighted by feature weighting method

and their weighted average value was taken as the threshold of pruning method to reduce the sparsity. Finally

energy characteristics were put into LS-SVM to train and predict.Experiments showed that this new method could fulfil adaptive classification of bearing fault signals and definite fault conclusion quickly and effectively.

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