基于EEMD与模糊信息熵的旋转机械故障诊断方法

兰州理工大学学报 ›› 2020, Vol. 46 ›› Issue (3): 39-44.

基于EEMD与模糊信息熵的旋转机械故障诊断方法

赵荣珍¹, 张琛^1,2, 邓林峰¹

1.兰州理工大学机电工程学院, 甘肃兰州 730050;
2.武警工程大学装甲车技术系, 新疆乌鲁木齐 840000

收稿日期:2018-05-23 出版日期:2020-06-28 发布日期:2020-08-19
作者简介:赵荣珍(1960-),女,山东枣庄人,博士,教授,博导.
基金资助:
国家自然科学基金(51675253)

Fault diagnosis method of rotating machinery based on both EEMD and fuzzy information entropy

ZHAO Rong-zhen¹, ZHANG Chen^1,2, DENG Lin-feng¹

1. College of Mechano-Electronic Engineering, Lanzhou Univ. of Tech., Lanzhou 730050, China;
2. Armored Vehicle Techology Department, Engineering University of PAP, Urumqi 840000, China

Received:2018-05-23 Online:2020-06-28 Published:2020-08-19

摘要/Abstract

摘要： 针对旋转机械故障识别率偏低的问题,提出一种基于EEMD与模糊信息熵的旋转机械故障诊断方法.该方法结合EEMD分解和模糊信息熵在特征提取方面的优势,构造出一种能够精细度量不同类别振动信号故障概率复杂度的特征集合.首先将原振动信号进行EEMD分解,获得若干个本征模态函数(IMFs);计算出前5个高频IMF分量的模糊信息熵组成高维特征集;利用LPP对高维特征集进行维数约简剔除冗余不相关特征;最后将约简后的样本集输入到KNN分类器中进行故障识别.用双跨转子实验台采集的数据对所述方法进行验证,并与EMD模糊熵、EMD模糊信息熵、EEMD模糊熵方法进行故障识别率对比,结果表明该方法能够有效提取转子振动信号的故障特征,并且具有更高的故障识别率.

关键词: 旋转机械, 故障诊断, EEMD, 模糊熵, 模糊信息熵

Abstract: Aimed at the lower accuracy of fault recognition of rotary machinery, the fault diagnosis method is proposed based on both EEMD and fuzzy information entropy. By using this method and incorporating with the superiority of EEMD and fuzzy information entropy in connection with feature extraction, a feature set is constructed, which can finely measure the complexity of the fault probability of different category of vibration signals. Firstly, the original vibration signal is decomposed with EEMD to obtain several intrinsic modality functions (IMFs), the fuzzy information entropy of the first 5 high-frequency IMF components is calculated to compose high-dimensional feature set then the LPP is used to reduce the dimensionality of the high-dimensional feature set and eliminate redundant irrelevant features, and finally, the reduced sample set is input into the KNN classifier to identify the faults. Foregoing method is validated by data collected from a double-span rotor test rig and compared with the methods of EMD fuzzy entropy, EMD fuzzy information entropy and EEMD fuzzy entropy in connection with the fault recognition accuracy. The result show that this method is able to extract effectively the fault feature of rotor vibration signals.It have higher fault recognition accuracy.

Key words: rotating machinery, fault diagnosis, EEMD, fuzzy entropy, fuzzy information entropy

中图分类号:

TH133
TH165

赵荣珍, 张琛, 邓林峰. 基于EEMD与模糊信息熵的旋转机械故障诊断方法[J]. 兰州理工大学学报, 2020, 46(3): 39-44.

ZHAO Rong-zhen, ZHANG Chen, DENG Lin-feng. Fault diagnosis method of rotating machinery based on both EEMD and fuzzy information entropy[J]. Journal of Lanzhou University of Technology, 2020, 46(3): 39-44.

参考文献

[1] DIALLO D,BENBOUZID M E H,HAMAD D,et al.Fault detection and diagnosis in an induction machine drive:apattern recognition approach based on concordia stator mean current vector [J].IEEE Transactions on Energy Conversion,2005,20(3):512-519.
[2] PENG Z K,CHU F L,TSE P W.Singularity analysis of the vibration signals by means of wavelet modulus maximal method [J].Mechanical Systems & Signal Processing,2007,21(2):780-794.
[3] LEI Y,ZUO M J,HE Z,et al.A multidimensional hybrid intelligent method for gear fault diagnosis [J].Expert Systems with Applications,2010,37(2):1419-1430.
[4] 杨望灿,张培林,王怀光,等.基于EEMD的多尺度模糊熵的齿轮故障诊断 [J].振动与冲击,2015,34(14):163-167.
[5] 赵荣珍,杨文瑛,马再超,等.信息熵与经验模态分解集成的转子故障信号量化特征提取 [J].兰州理工大学学报,2013,39(1):19-24.
[6] LEI Y,HE Z,ZI Y.Application of the EEMD method to rotor fault diagnosis of rotating machinery [J].Mechanical Systems & Signal Processing,2009,23(4):1327-1338.
[7] WU Z H,HUANG N E.Ensemble empirical mode decomposition: a noise-assisted data analysis method [J].Advances in Adaptive Data Analysis,2011,1(1):1-41.
[8] CHEN W,WANG Z,XIE H,et al.Characterization of surface EMG signal based on fuzzy entropy [J].IEEE Trans Neural Syst Rehabil Eng,2007,15(2):266-272.
[9] QU L,LI L,LEE J.Enhanced diagnostic certainty using information entropy theory [J].Advanced Engineering Informatics,2003,17(3/4):141-150.
[10] 艾延廷,费成巍,王志.航空发动机整机振动故障模糊信息熵诊断方法 [J].推进技术,2011,32(3):407-411.
[11] 郑直,姜万录,胡浩松,等.基于EEMD形态谱和KFCM聚类集成的滚动轴承故障诊断方法研究 [J].振动工程学报,2015(2):324-330.
[12] 赵荣珍,邓林峰.基于粗糙集数据分类概念的故障知识发现[J].振动、测试与诊断,2012,32(1):17-22.
[13] ZHANG X,LIANG Y,ZHOU J,et al.A novel bearing fault diagnosis model integrated permutation entropy,ensemble empirical mode decomposition and optimized SVM [J].Measurement,2015,69:164-179.
[14] 彭畅,柏林,谢小亮.基于EEMD、度量因子和快速峭度图的滚动轴承故障诊断方法 [J].振动与冲击,2012,31(20):143-146.
[15] ZHANG L,XIONG G,LIU H,et al.Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference [J].Expert Systems with Applications,2010,37(8):6077-6085.
[16] ZHENG J,CHENG J,YANG Y.A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy [J].Mechanism & Machine Theory,2013,70(6):441-453.