A new hybrid model of sparsity empirical wavelet transform and adaptive dynamic least squares support vector machine for fault diagnosis of gear pump

被引：12

作者：

Lu, Yan ^{[1
]}

Huang, Zhiping ^{[1
]}

机构：

[1] Quzhou Coll Technol, Dept Informat Engn, Quzhou 324000, Peoples R China

来源：

ADVANCES IN MECHANICAL ENGINEERING | 2020年 / 12卷 / 05期

关键词：

Sparsity empirical wavelet transform; adaptive dynamic least squares support vector machine; gear pump; fault diagnosis; hydraulic drive system; BEARING FAULT; CRACK-GROWTH; PREDICTION; CLASSIFICATION; SELECTION;

D O I：

10.1177/1687814020922047

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

Gear pump is the key component in hydraulic drive system, and it is very significant to fault diagnosis for gear pump. The combination of sparsity empirical wavelet transform and adaptive dynamic least squares support vector machine is proposed for fault diagnosis of gear pump in this article. Sparsity empirical wavelet transform is used to obtain the features of the vibrational signal of gear pump, the sparsity function is potential to make empirical wavelet transform adaptive, and adaptive dynamic least squares support vector machine is used to recognize the state of gear pump. The experimental results show that the diagnosis accuracies of sparsity empirical wavelet transform and adaptive dynamic least squares support vector machine are better than those of the empirical wavelet transform and adaptive dynamic least squares support vector machine method or the empirical wavelet transform and least squares support vector machine method.

引用

页数：8

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