基于KL-HVD的转子振动故障诊断方法研究

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摘要希尔伯特振动分解(Hilbert Vibration Decomposition，HVD)由于其虚假分量问题，严重制约了其在实际故障诊断中的应用。针对该问题，引入信息论中的K-L散度概念，提出了基于K-L散度的HVD虚假分量识别方法（KL-HVD）。KL-HVD将HVD分量视作概率分布各不相同的信号，并且认为真实分量与原信号的概率分布较为相近。该方法在原HVD方法基础上，计算HVD各分量与原信号的K-L散度值，对分量的虚假性进行量化。由于真假分量之间具有较大的差异性，本文选用高斯混合模型对这些分量进行聚类，自动区分出虚假分量并予以去除。此外，文中分别利用互信息及相关系数方法对虚假分量问题进行研究。并将三种方法应用于转子振动问题分析，结果显示三者中KL-HVD方法能够更有效地识别虚假分量，更清晰地提取出故障的时频特征。

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关键词 ：希尔伯特振动分解, 虚假分量, K-L 散度, 高斯混合模型, 振动故障诊断

Abstract：

The false components of HVD have seriously restricted its application in practical fault diagnosis.To solve this problem, Kullback-leibler (K-L) divergence was employed, which is the concept of information theory with HVD (KL-HVD).Components of HVD were treated as signals with various probability distributions, supposing that the probability distributions of real components are similar to the original signal.The false component identification method is based on K-L divergence (KL-HVD).KL-HVD, used in K-L divergence as a distinguishing index, was proposed to solve this problem.Based on the original HVD method, KL-HVD first calculates the K-L divergence values between the HVD components and the original signal and then clustered these values according to the Gaussian mixture model.Finally, the trues and the falses could automatically be separated from each other because of their intrinsic differences, and the false components will be eliminated.The results of the rotor fault signal analysis verify that KL-HVD divergence is more suitable for identifying the HVD false components than mutual information and the correlation coefficient method, and it could extract the faults’ time-frequency characteristics more clearly.

Key words： hilbert vibration decomposition fault component K-L divergence GMM vibration fault diagnosis

收稿日期: 2017-03-28 出版日期: 2018-08-15

引用本文:

朱霄珣，周沛，苑一鸣，徐博超，韩中合. 基于KL-HVD的转子振动故障诊断方法研究[J]. 振动与冲击, 2018, 37(16): 249-255.
ZHU Xiaoxun, ZHOU Pei, YUAN Yiming, XU Bochao, HAN Zhonghe. A study on the method of rotor vibration fault diagnosis based on KL-HVD. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(16): 249-255.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2018/V37/I16/249

[1] 程军圣，郑近德，杨宇. 基于局部特征尺度分解的经验包络解调方法及其在机械故障诊断中的应用[J]. 机械工程学报，2012，48(19)：87-94.
CHEN Junsheng, ZHENG Jinde, YANG Yu. Empirical Envelope Demodulation Approach Based on Local Characteristic-scale Decomposition and Its applicationsto Mechanical Fault Diagnosis [J]. Journal of Mechanical Engineering, 2012, 48(49)：87-94.
[2] FREI M G, OSORIO I. Intrinsic Time-scale Decomposition: Time Frequency-energy Analysis and Real-time Filtering of Non-stationary Signals[J]. Proc. Royal Soc. A, 2007, 463：321-342.
[3] 冯辅周，司爱威，饶国强，等. 基于小波相关排列熵的轴承早期故障诊断技术[J]. 机械工程学报，2012，48(13)：73-79
FENG Fuzhou, SI Aiwei, RAO Guoqiang, et al. Early Fault Diagnosis Technology for Bearing Based on Wavelet Correlation Permutation Entropy [J]. Journal of Mechanical Engineering, 2012，48(13)：73-79.
[4] 周颖涛，周绍骑，姚远航.减少模态混叠的改进EEMD算法[J].重庆理工大学学报（自然科学），2015，29(1)：111-115.
ZHOU Yintao, ZHOU Shaoqi, YAO yuanhang. Improved Ensemble Empirical Mode Decomposition to Reduce Modal Aliasing[J]. 2015, 29(1)：111-115.
[5] 程军圣, 张亢, 杨宇. 基于噪声辅助分析的总体局部均值分解方法[J]. 机械工程学报, 2011,47(3)：55-62.
CHENG Junsheng, ZHANG Kang, YANG Yu. Ensemble Local Mean Decomposition Method Based on Noise-assisted Analysis[J]. Journal of Mechanical Engineering, 2011, 47(3)：55-62.
[6] FELDMAN M. Time-varying Vibration Decomposition and Analysis based on The Hilbert Transform [J]. Journal of Sound and Vibration, 2006, 295(3/5)：518-530.
[7] 李利品，党瑞荣，樊养余. 改进的EEMD算法及其在多相流检测中的应[J]. 仪器仪表学报，2014(10)：2365-2371.
LI Lipin, DANG Ruirong, FAN Yangyu. Modified EEMD De-noising Method and its Application in Multiphase[J] Chinese Journal of Scientific Instrument, 2014(10)： 2365-2371.
[8] ZHANG M L, TANG J, ZHANG X M, et al. Intelligent Diagnosis of Short Hydraulic Signal Based on Improved EEMD and SVM with Few Low-dimensional Training Samples[J]. Chinese Journal of Mechanical Engineering，2016, 29(2)：396-405.
[9] 葛明涛，董素鸽.LMD及马氏距离敏感闽值的滚动轴承故障诊断[J]. 机械设计与制造，2015，2：210-213.
GE Mingtao,DONGSuge. Fault Diagnosis Method of Rolling Bearings Based on LMD and Mahalanobis Distance Sensitive Threshold[J]. Machinery Design & Manufacture, 2015, 2： 210-213.
[10] 朱可恒，宋希庚，薛冬新. 希尔伯特振动分解在滚动轴承故障诊断中应[J].振动与冲击，2014，33(14)：160-164.
ZHU Keheng, SONG Xigen, XUE Dongxin. Roller Bearing Fault Diagnosis Using Hilbert Vibration Decomposition [J]. Journal of Vibration and Shock, 2014, 33(14):160-164.
[11] 刘慧，刘国海，沈跃. 采用希尔伯特振动分解的非整数次谐波检测新方法[J].高压电技术，2009,35（7）：1758-1764.
LIU Hui, LIU Guohai, SHEN Yue. Novel Method for Non-integer Harmonics Measurement Using Hilbert Vibration Decomposition[J]. High Voltage Engineering, 2009,35(7)：1758-1764.
[12] MICHAEL FELDMAN. Theoretical Analysis and Comparison of the Hilbert Transform Decomposition Method[J]. Mechanical Systems and Signal Processing, 2008, 22(3)：509-519.
[13] N ABE, JI TAKEUCHI, MK WARMUTH. Polynomial Learnability of Probabilistic Concepts with Respect to the Kullback-Leibler Divergence[J]. Colt, 1991：277-289.
[14] 崔潇潇，王贵锦，林行刚. 基于Adaboost权值更新以及K-L距离的特征选择算[J]. 自动化学报，2009,35(5)：462-268.
CUI Xiaoxiao, WANG Guijin, LIN Xinggang. Feature Selection Based on Weight Updating and K-L Distance[J]. ActaAutomaticaSinica, 2009,35(5)：462-268.
[15] 王星.非参数统计[M].北京：清华大学出版社，2009.
WANG Xing. Non-parametric Statistics [M]. Beijing: Tsinghua University Press, 2009：207-211(in Chinese).
[16] JONES M, MARRON J, SHEATHER S. A brief survey of bandwidth selection for density estimation[J]. Journal of the American Statistical Association, 1996(91)： 401-407.
[17] 胡爱军．Hilbert-Huang 变换在旋转机械故障振动信号分析中的应用研究[D]．保定：华北电力大学，2008．
Hu Aijun．Research on the application of hilbert-huang transform in vibration signal analysis of rotating machinery[D]．Baoding：North China Electric Power University，2008(in Chinese)．
[18] 韩中合，朱霄珣，李文华.基于K-L散度的EMD虚假分量识别方法研[J].中国电机工程学报，2012，32(11)：112-119.
HAN Zhonghe, ZHU Xiaoxun, LI Wenhua. A False Component Identification Method of EMD Based on Kullback-leibler Divergence[J]. Proceeding of the CSEE, 2012, 32(11)：112-119.
[19] 杨世锡，焦卫东，吴昭同. 独立分量分析基网络应用于旋转机械故障特征抽取与分类[J] 机械工程学报，2004,40(3)：45-49.
YANG Shixi, JIAO Weidong, WU Zhaotong. Idependent Component Analysis Based Networks Forfault Feature Extraction and Classification of Ratating Machines[J]. CIESC Joural, 2004,40(3)：45-49.
[20] 夏向阳，李灵利，禹红，等. 电力系统中多谐波源的谐波分析方[J]. 中南大学学报(自然科学版),2014,45(12)：4239-4244.
XIA Xiangyang, LI Lingli, YU Hong, et al. Harmonic Analysis Method Based on Multi-harmonic Source in Power System[J]. Journal of Central South University (Science and Technology), 2014,45(12)：4239-4244.
[21] 王再英，白华宁.基于相关系数的过程系统故障检测与诊断方法[J].化工学报，2013，64(12)：4621-4627.
WANG Zaiying, BAI Huaning. Process System Fault Detection and Diagnosis Based on Correlation[J]. CIESC Journal, 2013, 64(12)：4621-4627.
[22] 赵志宏，杨绍普，申永军. 基于独立分量分析与相关系数的机械故障特征提取[J]. 振动与冲击，2013,32(6)：67-72.
ZHAO Zhihong，YANG Shaopu，SHEN Yongjun. Machinery Fault Feature Extraction Based on Independent Component Analysis and Correlation Coefficient[J]. Journal of Vibration and Shock, 2013,32(6)：67-72.
[23] 余爱华. 基于EM算法的高斯混合模型参数估计[J]. 现代计算机，2011,(6)：3-7.
YU Aihua. Design of Gaussian Mixture Parameter Based on EM Algorithm[J]. Modern Computer, 2011,(6)：3-7.
[24] 刘海涛，成玮，张学军. 联合正交变换与信号交织的测距仪脉冲干扰抑制方法[J]. 航空学报，2014,35(5)：1365-1373.
LIU Haitao，CHENG Wei，ZHANG Xuejun. DME Pulse Interference Mitigation Method Based on Joint Orthogonal Transform and Signal Interleaving[J]. ActaAeronautica et AstronauticaSinica2014,35(5):1365-1373.