Fault Diagnosis Based on FVMD Multi-scale Permutation Entropy and GK Fuzzy Clustering

doi:10.3901/JME.2018.14.016

Abstract

Abstract: Aiming at the nonlinear and non-stationary characteristics of equipment fault signals, a fault diagnosis method based on the fast variational mode decomposition (FVMD), the parameter optimized multi-scale permutation entropy and the feature weighted GK fuzzy clustering is proposed. Firstly, to reduce the running time and the number of iterations, the idea of fast iteration is introduced on the basis of variational mode decomposition, and the fast variational mode decomposition method is proposed. Secondly, aiming at the problem of parameter determination of multi-scale permutation entropy and considering the interaction among parameters comprehensively, a method of parameters optimization based on multi force particle swarm optimization is proposed, then the parameter optimized multi-scale permutation entropy is combined with fast variational mode decomposition for fault feature extraction. Thirdly, considering the contribution of each feature in the feature vector to the clustering process, a new method of GK fuzzy clustering based on ReliefF feature weighting is proposed, the feature weighted GK fuzzy clustering is used to determine the standard clustering center, and the fault pattern recognition is realized by the principle of choosing the nearest. Finally, the fault data of rolling bearing collected on the machinery fault simulator is taken as the research object, and the proposed method is applied to the analysis. The results show that compared with the original variational mode decomposition, multi-scale permutation entropy and GK fuzzy clustering, the proposed method not only can extract the fault features effectively, but also can realize the classification and recognition of fault modes accurately, and the fault recognition rate is improved.

Key words: fast variational mode decomposition, fault diagnosis, feature weighted GK fuzzy clustering, parameter optimized multi-scale permutation entropy

CLC Number:

TH165
TH133

CHEN Dongning, ZHANG Yundong, YAO Chengyu, SUN Fei, ZHOU Nengyuan. Fault Diagnosis Based on FVMD Multi-scale Permutation Entropy and GK Fuzzy Clustering[J]. Journal of Mechanical Engineering, 2018, 54(14): 16-27.

References

[1] 马萍,张宏立,范文慧. 基于局部与全局结构保持算法的滚动轴承故障诊断[J]. 机械工程学报,2017,53(2):20-25. MA Ping,ZHANG Hongli,FAN Wenhui. Fault diagnosis of rolling bearings based on local and global preserving embedding algorithm[J]. Journal of Mechanical Engineering,2017,53(2):20-25.
[2] 张云强,张培林,王怀光,等. 基于双时域微弱故障特征增强的轴承早期故障智能识别[J]. 机械工程学报,2016,52(21):96-103. ZHANG Yunqiang,ZHANG Peilin,WANG Huaiguang,et al. Rolling bearing early fault intelligence recognition based on weak fault feature enhancement in time-time domain[J]. Journal of Mechanical Engineering,2016,52(21):96-103.
[3] GABOR D. Theory of communication[J]. Journal of the Institution of Electrical Engine,1946,93(6):429-457.
[4] STAZEWSKI W J,TOMLINSON G R. Application of the wavelet transform to fault detection in a spur gear[J]. Mechanical Systems and Signal Processing,1994,8(3):289-307.
[5] HUANG N E,SHEN Z,LONG S R,et al. The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995.
[6] SMITH J S. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Interface,2005,2(5):443-454.
[7] COIFMAN R R,WICKERHAUSER M V. Entropy-based algorithms for best basis selection[J]. IEEE Transactions on Information Theory,1992,38(2):713-718.
[8] 杨斌,刘桂明,刘建友.基于标准互相关函数的经验模态分解端点效应处理方法[J]. 机械工程学报,2013,49(5):63-68. YANG Bin,LIU Guiming,LIU Jianyou. Method of empirical mode decomposition end effect based on standardized cross-correlation function[J]. Journal of Mechanical Engineering,2013,49(5):63-68.
[9] 程军圣,张亢,杨宇,等. 局部均值分解与经验模式分解的对比研究[J]. 振动与冲击,2009,28(5):13-16. CHENG Junsheng,ZHANG Kang,YANG Yu,et al. Comparison between the methods of local mean decomposition and empirical mode decomposition[J]. Journal of Vibration and Shock,2009,28(5):13-16.
[10] DRAGOMIRETSKIY K,ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing,2014,62(3):531-544.
[11] 唐贵基,王晓龙. 参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J]. 西安交通大学学报,2015,49(5):73-81. TANG Guiji,WANG Xiaolong. Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing[J]. Journal of Xi'an Jiaotong University,2015,49(5):73-81.
[12] 刘长良,武英杰,甄成刚. 基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J]. 中国电机工程学报,2015,35(13):3358-3365. LIU Changliang,WU Yingjie,ZHEN Chenggang. Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering[J]. Proceedings of the CSEE,2015,35(13):3358-3365.
[13] 武英杰,甄成刚,刘长良. 变分模态分解在风电机组故障诊断中的应用[J]. 机械传动,2015,39(10):129-132. WU Yingjie,ZHEN Chenggang,LIU Changliang. Application of variational mode decomposition in wind power fault diagnosis[J]. Journal of Mechanical,2015,39(10):129-132.
[14] 马增强,李亚超,刘政,等. 基于变分模态分解和Teager能量算子的滚动轴承故障特征提取[J]. 振动与冲击,2016,35(13):134-139. MA Zengqiang,LI Yachao,LIU Zheng,et al. Rolling bearings' fault feature extraction based on variational mode decomposition and Teager energy operator[J]. Journal of Vibration and Shock,2016,35(13):134-139.
[15] CANALES D P,RAMIREZ J A,CARLOS J,et al. Identification of dynamic instabilities in machining process using the approximate entropy method[J]. International Journal of Machine Tools and Manufacture,2011,51(6):556-564.
[16] AKTARUZZAMAN M,SASSI R. Parametric estimation of sample entropy in heart rate variability analysis[J]. Biomedical Signal Processing and Control,2014,14:141-147.
[17] BANDT C,POMPE B. Permutation entropy:a natural complexity measure for time series[J]. Physical Review Letters,2002,88(17):174102.
[18] AZIZ W,ARIF M. Multiscale permutation entropy of physiological times series[C]//IEEE INMIC International Multi-topic Conference,December 24-25,2005,Karachi,Pakistan. IEEE,2007:1-6.
[19] 郑近德,程军圣,杨宇. 多尺度排列熵及其在滚动轴承故障诊断中的应用[J]. 中国机械工程,2013,24(19):2641-2645. ZHENG Jinde,CHENG Junsheng,YANG Yu. Multi-scale permutation entropy and its applications to rolling bearing fault diagnosis[J]. China Mechanical Engineering,2013,24(19):2641-2645.
[20] 王余奎,李洪儒,叶鹏. 基于多尺度排列熵的液压泵故障识别[J]. 中国机械工程,2015,26(4):518-523. WANG Yukui,LI Hongru,YE Peng. Fault identification of hydraulic pump based on multi-scale permutation entropy[J]. China Mechanical Engineering,2015,26(4):518-523.
[21] 饶国强,冯辅周,司爱威,等. 排列熵算法参数的优化确定方法研究[J]. 振动与冲击,2014,33(1):188-193. RAO Guoqiang,FENG Fuzhou,SI Aiwei,et al. Method for optimal determination of parameters in permutation entropy algorithm[J]. Journal of Vibration and Shock,2014,33(1):188-193.
[22] DAVARI A,MARHABAN M H,NOOR S B M,et al. Parameter estimation of K-distributed sea clutter based on fuzzy inference and Gustafson-Kessel clustering[J]. Fuzzy Sets and Systems,2010,163(1):45-53.
[23] 王书涛,李亮,张淑清,等. 基于EEMD样本熵和GK模糊聚类的机械故障识别[J]. 中国机械工程,2013,24(22):3036-3044. WANG Shutao,LI Liang,ZHANG Shuqing,et al. Mechanical fault diagnosis method based on EEMD sample entropy and GK fuzzy clustering[J]. China Mechanical Engineering,2013,24(22):3036-3040.
[24] 杨志超,范立新,杨成顺,等. 基于GK模糊聚类和LS-SVC的GIS局部放电类型识别[J]. 电力系统保护与控制,2014,42(20):38-45. YANG Zhichao,FAN Lixin,YANG Chengshun,et al. Identification of partial discharge in gas insulated switchgears based on GK fuzzy clustering and LS-SVM[J]. Power System Protection and Control,2014,42(20):38-45.
[25] BECK A,TEBOULLE M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Sciences,2009,2(1):183-202.
[26] 陈东宁,张瑞星,姚成玉,等. 求解液压阀块加工车间调度的多作用力微粒群算法[J]. 中国机械工程,2015,26(3):369-378. CHEN Dongning,ZHANG Ruixing,YAO Chengyu,et al. A MFPSO algorithm for solving hydraulic manifold processing shop scheduling[J]. China Mechanical Engineering,2015,26(3):369-378.
[27] 张龙,黄文艺,熊国良. 基于多尺度熵的滚动轴承故障程度评估[J]. 振动与冲击,2014,33(9):185-189. ZHANG Long,HUANG Wenyin,XIONG Guoliang. Assessment of rolling element bearing fault severity using multi-scale entropy[J]. Journal of Vibration and Shock,2014,33(9):185-189.
[28] KONONENKO I. Estimating attributes:analysis and extensions of Relief[C]//European Conference on Machine Learning,April 6-8, 1994,Catania,Italy. Berlin:Springer,1994:171-182.
[29] 李洁,高新波,焦李成. 基于特征加权的模糊聚类新算法[J]. 电子学报,2006,1(1):89-92. LI Jie,GAO Xinbo,JIAO Licheng. A new feature weighted fuzzy clustering algorithm[J]. Acta Electronica Sinica,2006,1(1):89-92.