Volume 33 Issue 10
Oct.  2018
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Rolling bearing fault diagnosis based on IITD and FCM clustering[J]. Journal of Aerospace Power, 2018, 33(10): 2553-2560. doi: 10.13224/j.cnki.jasp.2018.10.029
Citation: Rolling bearing fault diagnosis based on IITD and FCM clustering[J]. Journal of Aerospace Power, 2018, 33(10): 2553-2560. doi: 10.13224/j.cnki.jasp.2018.10.029

Rolling bearing fault diagnosis based on IITD and FCM clustering

doi: 10.13224/j.cnki.jasp.2018.10.029
  • Received Date: 2017-05-24
  • Publish Date: 2018-10-28
  • An improved intrinsic time-scale decomposition (IITD) method was proposed based on Akima interpolation and linear transformation of intrinsic time-scale decomposition (ITD). Furthermore, based on approximate entropy (AE) and fuzzy C-means clustering (FCM), a new analysis method of using IITD for fault vibration signal of rolling bearing was proposed as well. The vibration signal was decomposed with IITD to obtain a certain number of proper rotation (PR) and a trend. By using mutual information analysis, three PR components were sifted out and the AE was calculated as the eigenvectors. The constructed eigenvectors were put into FCM classifier to recognize different fault types. These results were compared with the methods based on empirical mode decomposition (EMD) and ITD approximate entropy and FCM respectively. The classification coefficient with use of this method was calculated more closer to 1 and average fuzzy entropy was calculated more closer to 0. Accurate fault identification was presented for roller bearings normal, inner faults, outer faults, rolling body fault signals and different damage degree signals of rolling body faults.

     

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  • [1]
    郑红,周雷,杨浩.基于小波包分析与多核学习的滚动轴承故障诊断[J].航空动力学报,2015,30(12):3035-3042.ZHENG Hong,ZHOU Lei,YANG Hao.Roller bearing fault diagnosis based on wavelet packet analysis and multi kernel learning[J].Journal of Aerospace Power,2015,30(12):3035-3042.(in Chinese)
    [2]
    向丹,岑建.基于EMD熵特征融合的滚动轴承故障诊断方法[J].航空动力学报,2015,30(5):1149-1155.XIANG Dan,CEN Jian.Method of roller bearing fault diagnosis based on feature fusion of EMD entropy[J].Journal of Aerospace Power,2015,30(5):1149-1155.(in Chinese)
    [3]
    王军辉,贾嵘,谭泊.基于EEMD和模糊C均值聚类的风电机组齿轮箱故障诊断[J].太阳能学报,2015,36(2):319-324.WANG Junhui,JIA Rong,TAN Bo.Fault diagnosis of windturbines gearbox based on EEMD and fuzzy C means clustering[J].Acta Energiae Solaris Sinica,2015,36(2):319-324.(in Chinese)
    [4]
    杨宇,王欢欢,程军圣,等.基于LMD的包络谱特征值在滚动轴承故障诊断中的应用[J].航空动力学报,2012,27(5):1153-1158.YANG Yu,WANG Huanhuan,CHENG Junsheng,et al.Application of envelope spectrum characteristics method based on LMD to roller bearing fault diagnosis[J].Journal of Aerospace Power,2012,27(5):1153-1158.(in Chinese)
    [5]
    段礼祥,张来斌,岳晶晶.基于ITD和模糊聚类的齿轮箱故障诊断方法[J].中国石油大学学报(自然科学版),2013,37(4):133-139.DUAN Lixiang,ZHANG Laibin,YUE Jingjing.Fault diagonsis method of gearbox based on intrinsic time-scale decomposition and fuzzy clustering[J].Journal of China University of Petroleum (Edition of Natural Science),2013,37(4):133-139.(in Chinese)
    [6]
    HUANG N E,SHEN Z,LONG S R,et al.A new view of nonlinear waves:the Hilbert spectrum[J].Annual Review of Fluid Mechanics,1999,31(1):417-457.
    [7]
    YANG Y,CHENG J S,ZHANG K.An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems[J].Measurement,2012,45(3):561-570.
    [8]
    WU Z H,HUANG N E.Ensemble empirical mode decomposition:a noise assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
    [9]
    FREI M G,OSORTO I.Intrinsic time-scale decomposition:time-frequency-energy analysis and realtime filtering of non-stationary signals[J].Proceeding of the Royal Society A,2007,463(2078):321-342.
    [10]
    向玲,鄢小安.基于小波包的EITD风力发电机组齿轮箱故障诊断[J].动力工程学报,2015,35(3):205-212.XIANG Ling,YAN Xiaoan.Fault diagnosis of windturbine gearbox based on EITD-WPT method[J].Journal of Chinese Society of Power Engineering,2015,35(3):205-212.(in Chinese)
    [11]
    胥永刚,何正嘉.分形维数和近似熵用于度量信号复杂性的比较研究[J].振动与冲击,2009,28(5):13-16.XU Yonggang,HE Zhengjia.Research on comparison between approximate entropy and fractal dimension for complexity measure of signals[J].Journal of Vibration and Shock,2009,28(5):13-16.(in Chinese)
    [12]
    PINCUS S M.Approximate entropy as a measure of system complexity[J].Proceeding of the National Academy Sciences of the United States of America,1991,88(6):2297-2301.
    [13]
    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.(in Chinese)
    [14]
    PAL N R,BEZDEK J C.On cluster validity for the fuzzy C-means model[J].IEEE Transactions on Fuzzy Systems,1995,3(3):370-379.
    [15]
    BEZDEK J C.Cluster validity with fuzzy sets[J].Journal of Cybernetics,1974,3(3):58-72.
    [16]
    胡爱军.Hilbert-Huang变换在旋转机械振动信号分析中的应用研究[D].河北 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 Hebei:North China Electric Power University,2008.(in Chinese)
    [17]
    PENG Z K,TSEP W,CHU F L.A comparison study of improved Hilbert-Huang transform and wavelet transform:application to fault diagnosis for rolling bearing[J].Mechanical Systems and Signal Processing,2005,19(5):974-988.
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