[1]李军,李佳,张世义,等.采用EEMD算法与互信息法的机械故障诊断方法[J].华侨大学学报(自然科学版),2018,39(1):7-13.[doi:10.11830/ISSN.1000-5013.201706091]
　LI Jun,LI Jia,ZHANG Shiyi,et al.Mechanical Fault Diagnosis Method Using EEMD Algorithm and Mutual Information Method[J].Journal of Huaqiao University(Natural Science),2018,39(1):7-13.[doi:10.11830/ISSN.1000-5013.201706091]
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采用EEMD算法与互信息法的机械故障诊断方法()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第39卷
期数:
2018年第1期
页码:
7-13
栏目:
出版日期:
2018-01-17

文章信息/Info

Title:
Mechanical Fault Diagnosis Method Using EEMD Algorithm and Mutual Information Method
文章编号:
1000-5013(2018)01-0007-07
作者:
李军1; 2;  李佳1;  张世义1;  束海波1; 2
1. 重庆交通大学 机电与车辆工程学院, 重庆 400074;2. 重庆交通大学 城市轨道交通车辆系统集成与控制重庆市重点实验室, 重庆 400074
Author(s):
LI Jun1; 2;  LI Jia1;  ZHANG Shiyi1;  SHU Haibo1; 2
1. College of Mechantronics and Automotive Engineering, Chongqing Jiaotong University, Chongqing 400074, China; 2. Chongqing Key Laboratory of System Integration and Control for Urban Rail Transit Vehicle, Chongqing Jiaotong University, Chongqing 400074, China
关键词:
故障诊断;  固有模态函数;  Hilbert-Huang变换;  总体经验模态分解;  互信息法
Keywords:
fault diagnosis;  intrinsic modal function;  Hilbert-Huang transform;  ensemble empirical mode decomposition;  mutual information
分类号:
TH165.3
DOI:
10.11830/ISSN.1000-5013.201706091
文献标志码:
A
摘要:
提出一种总体经验模态分解(EEMD)算法与互信息法相结合的Hilbert-Huang变换机械故障诊断改进的方法.仿真与实例结果表明:EEMD算法能克服模态混叠弊端,获得具有实际物理含义的固有模态函数(IMF);互信息法能有效剔除虚假分量,使最终IMF分量更加精准且集中突显故障信号特征;所提出方法能有效表征机械故障特征,并进行精确诊断.
Abstract:
An improved Hilbert-Huang transform method for fault diagnosis was proposed which combined ensemble empirical mode decomposition(EEMD)algorithm and mutual information method. The simulation and example results showed that EEMD algorithm can overcome the drawbacks of modal aliasing and obtain the intrinsic modal function(IMF)with practical physical meanings and the mutual information method can effectively eliminate the false components, which makes the final IMF components more accurate and more concentrated on the fault signal characteristics. The proposed method can effectively characterize the mechanical failure and realize accurate diagnosis.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2017-06-30
通信作者: 李军(1964-),男,教授,博士,主要从事汽车发动机排放与控制、节能与新能源汽车的研究.E-mail:cqleejun@sina.com.
基金项目: 国家自然科学基金资助项目(51305472); 重庆市自然科学基金重大资助项目(CSTC2015ZDCY_ZDZX60010); 重庆市重点实验室科研基金资助项目(CSTC2015YFPT_ZDSYS3000); 重庆市教委科技计划项目(KJ120423)
更新日期/Last Update: 2018-01-20