|本期目录/Table of Contents|

[1]曾超俊,王荣杰,王亦春,等.一种基于EMD-BLS的三相整流电路故障诊断方法[J].集美大学学报(自然科学版),2021,26(4):357-364.
　ZENG Chaojun,WANG Rongjie,WANG Yichun,et al.A Fault Diagnosis Method of Three-Phase Rectifier Based on Empirical Mode Decomposition and Broad Learning System[J].Journal of Jimei University,2021,26(4):357-364.
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一种基于EMD-BLS的三相整流电路故障诊断方法(PDF)
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《集美大学学报（自然科学版）》[ISSN:1007-7405/CN:35-1186/N]

卷:
第26卷
期数:
2021年第4期
页码:
357-364
栏目:
船舶与机械工程
出版日期:
2021-07-28

文章信息/Info

Title:
A Fault Diagnosis Method of Three-Phase Rectifier Based on Empirical Mode Decomposition and Broad Learning System
作者:
曾超俊1; 王荣杰1; 2; 王亦春1; 2; 郜怀通1; 林安辉1; 2; 韩冉1
(1.集美大学轮机工程学院，福建 厦门 361021；2.福建省船舶与海洋工程重点实验室，福建 厦门 361021)
Author(s):
ZENG Chaojun1; WANG Rongjie1; 2; WANG Yichun1; 2; GAO Huaitong1; LIN Anhui1; 2 ; HAN Ran1
(1.School of Marine Engineering,Jimei University,Xiamen 361021,China;2.Fujian Provincial Key Laboratory of Naval Architecture and Ocean Engineering,Xiamen 361021,China)
关键词:
三相整流电路; 故障诊断; 经验模态分解; 宽度学习系统
Keywords:
three-phase rectifier; fault diagnosis; empirical mode decomposition; broad learning system
分类号:
-
DOI:
-
文献标志码:
A
摘要:
针对三相整流电路的故障诊断，提出了一种基于经验模式分解和宽度学习系统相结合的三相整流电路故障诊断方法。首先利用经验模式分解方法对故障信号进行分解，提取基本模式分量的能量作为特征信号;然后再利用时间复杂度、低分类高精度的宽度学习系统建立故障诊断的分类模型，有效地完成三相整流电路的故障分类。实验结果表明，经验模式分解特征提取效果显著,宽度学习系统故障分类器具有较好的适应性，较快的计算速度和较高的准确度。
Abstract:
For the fault diagnosis of three-phase rectifier circuit,this paper proposes a fault diagnosis method of three-phase rectifier circuit based on the combination of empirical mode decomposition and broad learning system.The method first uses the empirical mode decomposition method to decompose the fault signal and extract the basic mode energy of the component as the characteristic signal,and then the broad learning system with low time complexity and high classification accuracy is used to establish classification model for fault diagnosis,which effectively accomplish the fault classification of the three-phase rectifier circuit.The experimental results show that the empirical mode decomposition feature extraction effect is significant,the broad learning system fault classifier has better adaptability,faster calculation speed and higher accuracy.

参考文献/References:

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备注/Memo

备注/Memo:
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更新日期/Last Update: 2021-09-19