基于 LMD 多分量样本熵融合和 LS-SVM 的矿车轮毂驱动系统故障诊断

IF 2.2 3区 工程技术 Q2 ENGINEERING, MECHANICAL
Actuators Pub Date : 2023-12-16 DOI:10.3390/act12120468
Le Xu, Wei Li, Bo Zhang, Yubin Zhu, Chaonan Lang
{"title":"基于 LMD 多分量样本熵融合和 LS-SVM 的矿车轮毂驱动系统故障诊断","authors":"Le Xu, Wei Li, Bo Zhang, Yubin Zhu, Chaonan Lang","doi":"10.3390/act12120468","DOIUrl":null,"url":null,"abstract":"As the main transportation equipment in ore mining, the wheel drive system of mining trucks plays a crucial role in the transportation capacity of mining trucks. The internal components of the hub drive system are mainly composed of bearings, gears, etc. The vibration signals caused during operation are nonlinear and nonstationary complex signals, and there may be more than one factor that causes faults, which causes certain difficulties for the fault diagnosis of the hub drive system. A fault diagnosis method based on local mean decomposition (LMD) multi-component sample entropy fusion and LS-SVM is proposed to address this issue. Firstly, the LMD method is used to decompose the vibration signals in different states to obtain a finite number of PF components. Then, based on the typical correlation analysis method, the distribution characteristics and correlation coefficients of vibration signals in the frequency domain under different states are calculated, and effective PF multi-component sample entropy features are constructed. Finally, the LS-SVM multi-fault classifier is used to train and test the extracted multi-component sample entropy features to verify the effectiveness of the method. The experimental results show that, even in small-sample data, the LMD multi-component sample entropy fusion and LS-SVM method can accurately extract fault features of vibration signals and complete classification, achieving fault diagnosis of wheel drive systems.","PeriodicalId":48584,"journal":{"name":"Actuators","volume":"12 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2023-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fault Diagnosis of Mine Truck Hub Drive System Based on LMD Multi-Component Sample Entropy Fusion and LS-SVM\",\"authors\":\"Le Xu, Wei Li, Bo Zhang, Yubin Zhu, Chaonan Lang\",\"doi\":\"10.3390/act12120468\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As the main transportation equipment in ore mining, the wheel drive system of mining trucks plays a crucial role in the transportation capacity of mining trucks. The internal components of the hub drive system are mainly composed of bearings, gears, etc. The vibration signals caused during operation are nonlinear and nonstationary complex signals, and there may be more than one factor that causes faults, which causes certain difficulties for the fault diagnosis of the hub drive system. A fault diagnosis method based on local mean decomposition (LMD) multi-component sample entropy fusion and LS-SVM is proposed to address this issue. Firstly, the LMD method is used to decompose the vibration signals in different states to obtain a finite number of PF components. Then, based on the typical correlation analysis method, the distribution characteristics and correlation coefficients of vibration signals in the frequency domain under different states are calculated, and effective PF multi-component sample entropy features are constructed. Finally, the LS-SVM multi-fault classifier is used to train and test the extracted multi-component sample entropy features to verify the effectiveness of the method. The experimental results show that, even in small-sample data, the LMD multi-component sample entropy fusion and LS-SVM method can accurately extract fault features of vibration signals and complete classification, achieving fault diagnosis of wheel drive systems.\",\"PeriodicalId\":48584,\"journal\":{\"name\":\"Actuators\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2023-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Actuators\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3390/act12120468\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Actuators","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/act12120468","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0

摘要

作为矿石开采的主要运输设备,矿用卡车的轮毂驱动系统对矿用卡车的运输能力起着至关重要的作用。轮毂驱动系统的内部组件主要由轴承、齿轮等组成。运行过程中产生的振动信号是非线性、非稳态的复杂信号,引起故障的因素可能不止一个,这给轮毂驱动系统的故障诊断带来了一定的困难。针对这一问题,提出了一种基于局部均值分解(LMD)多分量样本熵融合和 LS-SVM 的故障诊断方法。首先,利用 LMD 方法对不同状态下的振动信号进行分解,得到有限数量的 PF 分量。然后,基于典型相关分析方法,计算不同状态下振动信号在频域中的分布特征和相关系数,构建有效的 PF 多分量样本熵特征。最后,利用 LS-SVM 多故障分类器对提取的多分量样本熵特征进行训练和测试,以验证该方法的有效性。实验结果表明,即使在小样本数据中,LMD 多分量样本熵融合和 LS-SVM 方法也能准确提取振动信号的故障特征并完成分类,实现对车轮驱动系统的故障诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault Diagnosis of Mine Truck Hub Drive System Based on LMD Multi-Component Sample Entropy Fusion and LS-SVM
As the main transportation equipment in ore mining, the wheel drive system of mining trucks plays a crucial role in the transportation capacity of mining trucks. The internal components of the hub drive system are mainly composed of bearings, gears, etc. The vibration signals caused during operation are nonlinear and nonstationary complex signals, and there may be more than one factor that causes faults, which causes certain difficulties for the fault diagnosis of the hub drive system. A fault diagnosis method based on local mean decomposition (LMD) multi-component sample entropy fusion and LS-SVM is proposed to address this issue. Firstly, the LMD method is used to decompose the vibration signals in different states to obtain a finite number of PF components. Then, based on the typical correlation analysis method, the distribution characteristics and correlation coefficients of vibration signals in the frequency domain under different states are calculated, and effective PF multi-component sample entropy features are constructed. Finally, the LS-SVM multi-fault classifier is used to train and test the extracted multi-component sample entropy features to verify the effectiveness of the method. The experimental results show that, even in small-sample data, the LMD multi-component sample entropy fusion and LS-SVM method can accurately extract fault features of vibration signals and complete classification, achieving fault diagnosis of wheel drive systems.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Actuators
Actuators Mathematics-Control and Optimization
CiteScore
3.90
自引率
15.40%
发文量
315
审稿时长
11 weeks
期刊介绍: Actuators (ISSN 2076-0825; CODEN: ACTUC3) is an international open access journal on the science and technology of actuators and control systems published quarterly online by MDPI.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信