Compound fault features separation diagnosis method based on complex network community clustering algorithm

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Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (7) : 76-81.

Chen Anhua, Mo Zhijun，Jiang Lingli,Yang Pan

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Abstract

The diagnosis of composite fault occurred in mechanism is a difficult challenge at present. Due to fault feature of composite failure each other interference, it is hard to composite fault of full and accurate diagnose. This paper presents a diagnosis method that compound fault features separation based on complex network community clustering algorithm. The author firstly do that fault signal is decomposed into several intrinsic mode functions by empirical mode decomposition adaptive. Due to different single fault characteristics will be reflected in different frequency, the author extracts the characteristics of each IMF component and establishes the network model of fault data. We will each intrinsic mode function as the complex network community. According to the characteristics of complex network community structure, we will each intrinsic mode function as the complex network community. Similar community will be merged, which each merge community corresponding different single fault. The last, we will get each community corresponding to different single fault, so as to realize the separation of compound fault. Take the rotor unbalance and bearing inner, bearing inner and bearing roller fault separation characteristics and diagnosis for example, the author proves feasibility of this method.

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complex network / community clustering / composite fault / separation characteristics / fault diagnosis

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Chen Anhua, Mo Zhijun，Jiang Lingli,Yang Pan. Compound fault features separation diagnosis method based on complex network community clustering algorithm[J]. Journal of Vibration and Shock, 2016, 35(7): 76-81

References

[1] 明安波,褚福磊,张炜. 滚动轴承复合故障特征分离的小波-频谱自相关方法[J].机械工程学报,2013,49(3): 80-86.
MING Anbo，CHU Fulei，ZHANG Wei.Compound Fault Features Separation of Rolling Element Bearing Based on the Wavelet Decomposition and Spectrum Auto-correlation[J]. Journal of Mechanical Engineering,
2013,49(3): 80-86.
[2] TANG Gui-Ji, HE Yu-Ling, WAN Shu-Ting, etc. Investigation on stator vibration characteristics under air-gap eccentricity and rotor short circuit composite faults[J].The Brazilian Society of Mechanical Sciences and Engineering,2014,36:511-522.
[3] 蒋刚,肖建,郑永康等.一种改进的快速傅里叶变换算法及其在故障诊断中的应用[J]. 计算机应用,2006, 26(2) : 448-450.
JIANG Gang, XIAO Jian, ZHENG Yong-kang, SONG Chang-lin.Fixed structure FFT algorithm and application in fault diagn-oses[J].Computer Applications,2006, 26(2) : 448- 450.
[4] JING Tian, Carlos Morillo, Michael G. Pecht. Rolling Element Bearing Fault Diagnosis Using Simulated Annealing Optimized Spectral Kurtosis[J].Center for Advanced Life Engineering, 2013,1-5.
[5] 段晨东,何正嘉. 基于提升模式的特征小波构造及其应用[J].振动工程学报,2007,20(1):85-90.
DUAN Chen-dong, HE Zheng-jia. Characterized wavelet constr-uction based on lifting scheme and its applications[J].Journal of Vibration Engineering,2007,20
(1):85-90.
[6] ANTONI J. The spectral kurtosis：A useful tool for characterizing non-stationary signals[J]. Mechanical Systems and Signal Processing,2006,20(2):282-307.
[7] 杜海峰,王娜,张进华等.基于复杂网络的故障诊断策略[J].机械工程学报,2010,46(3): 90-96.
DU Hai feng, WANG Na, ZHANG Jin hua,etal.Fault Diagnosis Strategy Based on Complex Network Analysis[J].Journal of Mechanical Engineering,2010,46
(3): 90-96.
[8] 张爱萍.复杂网络的仿真研究及在轮机系统中的应用[D].大连:大连海事大学,2010.
ZHANG Ai-ping. Research in Simulation and Applications to Marine System of Complex Networks[D]. Da lian: Da lian Maritime University, 2010.
[9] Newman M E J. The structure and function of complex network [J].SIAM Review, 2003 ,45(2):167-256.
[10] LI Xiang-hua, GAO Chao, PU Ru-yang. A Community Clustering Algorithm Based on Genetic Algorithm With Novel Coding Scheme[J].International Conference on Natural Computation,2014,10: 486-491.
[11] HUANG N E,SHEN Z,LONG S R,etal. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Royal Society of London Proceedings Series A,1998,454(3) :1971.
[12] 蒋玲莉,刘义伦,李学军等.EMD-模糊聚类法及在滚动轴承故障诊断中的应用[J].机械强度.2011,33(1).
JIANG Ling Li,LIU Yi Lun, LI Xue Jun,etal.Fault diagnosis of roller bearing based on EMD and Fuzzy cluster[J].Journal of Mechanical Strength.2011,33(1).
[13] ZHAO Ling, CHU Xiao-yan, HUANG Da-rong. Fault Diagnosis for Gearbox Based on EMD and Multiracial[J].Chinese Control and Decision Conference,2014,26:3792-3796.
[14] 汪小帆,李翔,陈关荣.复杂网络理论及其应用[M]:清华大学出牌社,2006.
WANG Xiao fan, LI Xiang, CHEN Guan rong. Theory and application of Complex network[M]: Tsinghua University press, 2006.
[15] Tabak B M, Takami M, Rocha J M C, etal. Directed clustering coefficient as a measure of systemic risk in complex banking networks[J]. Physica A: Statistical Mechanics and its Applications, 2014, 394: 211-216.
[16] NEWMAN M E J. Fast algorithm for detecting community structure in networks [J].Phys Rev.E,2004(69),066133:1-5.
[17] 杜海峰,悦中山,李树茁等.基于模块性指标的动态网络社群结构探测方法[J].系统工程理论与实践.2009, 29(3),163-171.
DU Hai feng, YUE zhons shan, Ll Shu zhuo,etal.Community structure detecting algorithm for dynamic networks based on modularity[J].Systems Engineering—Theory&Practice. 2009, 29(3),163-171.