|本期目录/Table of Contents|

[1]田慕琴,王秀秀,宋建成,等. 笼型异步电动机转子断条故障诊断方法[J].电机与控制学报,2015,19(06):14-21.[doi:10. 15938/j. emc.2015.06.003]
 TIAN Mu-qin,WANG Xiu-xiu,SONG Jiancheng,et al. Diagnosis method of rotor bar broken faultin cage asynchronous motor[J].,2015,19(06):14-21.[doi:10. 15938/j. emc.2015.06.003]
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 笼型异步电动机转子断条故障诊断方法(PDF)
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
19
期数:
2015年06
页码:
14-21
栏目:
出版日期:
2015-06-15

文章信息/Info

Title:
 Diagnosis method of rotor bar broken fault
in cage asynchronous motor
作者:
 田慕琴王秀秀宋建成吝伶艳李传扬张福亮
 1.太原理工大学 煤矿装备与安全控制山西省重点实验室,山西太原030024
2.山西昌生电磁线有限公司,山西太原030024
Author(s):
 TIAN Mu-qinWANG Xiu-xiuSONG Jian一chengLIN Ling-yanLI Chuan-yangZHANG Fu-liang
 1.Shanxi Key Laboratory of Coal Minim Equipment and Safety Control, Taiyuan University of Technology,Taiyuan0 030024,China;
2. Chang Sheng Shanxi Electromagnetic Wire Limited Company, Taiyuan 030024,China
关键词:
 笼型异步电动机断条Hilbert变换支持向量机故障诊断
Keywords:
 Keywords:cage asynchronous motor broken bar Hilbert transformsupport vector machinesfault diagnoses
分类号:
TM 343
DOI:
10. 15938/j. emc.2015.06.003
文献标志码:
A
摘要:
 摘要:针对笼型异步电动机发生转子断条故障时,用于判定故障类型及其严重程度的定子电流信
号中的边频信号容易被主频信号所淹没的问题,研究了一种基于Hilbert变换和支持向量机理论的
笼型异步电动机断条故障诊断方法。首先进行了详细的理论推导,为该方法在断条故障诊断中的
应用奠定了基础。然后设计并完成了一系列断条故障试验,取得了真实有效的故障数据。最后,将
该方法应用于试验数据的分析与处理,结果表明Hilbert变换能有效提取到断条故障时定子电流信
号中的故障特征量,而采用这些特征量训练得到的支持向量机分类模型则能在故障样本有限的前
提下实现最优分类,将二者结合起来用于断条故障诊断的准确率高达98% 。
Abstract:
 Abstract:When the cage induction motor rotor broken bar fault occurs,side-band signal is generated in
the stator current signal. That is why the component is often used to determine whether the rotor bar suffer
broken fault and how serious it is. However,the side-band signal is easily overwhelmed by main frequen-
cy signal. To solve this problem,a kind of fault diagnosis method based on Hilbert-SVM(Support Vector
Machines)was researched. First,detailed theoretical derivation laid the foundation for the application.
Then,through designing and completing a series of broken bar fault testing,the real and effective fault
data were obtained. Finally,this method was used to analyze and process testing data. The results show
that fault characteristic quantities from stator current signal when rotor bar goes wrong could been effec-
tively extracted through Hilbert transform,and on the premise of limited fault samples,optimal classifica-
tion could been achieved through SVM classification model by training the characteristic quantities. As a
result,the accuracy rate of this fault diagnosis method,which combine Hilbert transform and SVM,is
high as 98 %.

参考文献/References:

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[11]田慕琴,王秀秀,宋建成,等. 笼型异步电动机转子断条故障诊断方法[J].电机与控制学报,2015,19(06):14.[doi:10. 15938/j. emc.2015.06.003]
 TIAN Mu-qin,WANG Xiu-xiu,SONG Jiancheng,et al. Diagnosis method of rotor bar broken faultin cage asynchronous motor[J].,2015,19(06):14.[doi:10. 15938/j. emc.2015.06.003]

备注/Memo

备注/Memo:
 基金项目:高等学校博士学科点专项科研基金资助项目(20111402110010);国家自然科学基金面上项目(51377113)
更新日期/Last Update: 2015-07-24