A gear fault diagnosis method based on variational mode decomposition and multi-scale discrete entropy

IF 0.7 Q4 ENGINEERING, MECHANICAL Journal of Vibroengineering Pub Date : 2024-01-21 DOI:10.21595/jve.2023.23515
Tao Zhang, Yongqi Chen, Yang Chen, Qianqian Shen, Qinge Dai
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引用次数: 0

Abstract

Aiming at monitoring of gearbox faults, a gear fault feature extraction method based on variational mode decomposition (VMD) and multi-scale discrete entropy (MDE) is proposed in this paper. Firstly, the gear fault signal is decomposed into a series of intrinsic modal function (IMF) by VMD with selected parameters; Secondly, the decomposed IMF are extracted by MDE feature extraction method to form a feature sample set; Finally, the least square support vector machine (LSSVM) is used to classify the data set after feature extraction. The experiment results show that the proposed method owns the higher fault diagnosis accuracy than the traditional multi-scale entropy methods.
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基于变异模式分解和多尺度离散熵的齿轮故障诊断方法
针对齿轮箱故障监测,本文提出了一种基于变异模态分解(VMD)和多尺度离散熵(MDE)的齿轮故障特征提取方法。首先,利用 VMD 将齿轮故障信号分解为一系列本征模态函数(IMF),并选取相应参数;其次,利用 MDE 特征提取方法将分解后的 IMF 提取出来,形成特征样本集;最后,利用最小平方支持向量机(LSSVM)对特征提取后的数据集进行分类。实验结果表明,与传统的多尺度熵方法相比,所提出的方法具有更高的故障诊断精度。
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来源期刊
Journal of Vibroengineering
Journal of Vibroengineering 工程技术-工程:机械
CiteScore
1.70
自引率
0.00%
发文量
97
审稿时长
4.5 months
期刊介绍: Journal of VIBROENGINEERING (JVE) ISSN 1392-8716 is a prestigious peer reviewed International Journal specializing in theoretical and practical aspects of Vibration Engineering. It is indexed in ESCI and other major databases. Published every 1.5 months (8 times yearly), the journal attracts attention from the International Engineering Community.
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