Journal of Central South University

, Volume 24, Issue 5, pp 1155–1163 | Cite as

Assessment of bearing performance degradation via extension and EEMD combined approach

  • Yu-mei Liu (刘玉梅)
  • Cong-cong Zhao (赵聪聪)
  • Ming-ye Xiong (熊明烨)
  • Ying-hui Zhao (赵颖慧)
  • Ning-guo Qiao (乔宁国)
  • Guang-dong Tian (田广东)


As a key component in rotating machinery, the operating reliability of bearing influences the performance and service life of the equipment directly. In order to describe bearing performance degradation (BPD) process effectively, an assessment approach combining extension and ensemble empirical mode decomposition (EEMD) was proposed. First, the extension was utilized to construct the matter-element of bearing operating state, and the energy moment of intrinsic mode functions (IMFs) was used as characteristic parameter of the matter-element. Then, to determine classical domains of characteristic parameters, the mathematical statistics method was adopted. Finally, the BPD was analyzed qualitatively and quantitatively according to the comprehensive correlation degree of bearing current operating state related to its healthy state. The analytic results of bearing test-rig show that the proposed method indicates the incipient fault approximately occuring in the 81st hour, and the method also quantitatively presents the degree of BPD. By contrast, the BPD assessment based on time-domain features extraction method could not achieve the above two results effectively.

Key words

bearing performance degradation assessment extension ensemble empirical mode decomposition mathematical statistics 


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  1. [1]
    ZHANG Chao, CHEN Jian-jun, XU Ya-lan. A bearing fault diagnosis method based on EMD and difference spectrum theory of singular value [J]. Journal of Vibration Engineering, 2011, 24(5): 539–545. (in Chinese)Google Scholar
  2. [2]
    PENG Tao, YANG Hui-bin, LI Jian-bao, JIANG Hai-yan, WEI Wei. Mixed-domain feature extraction approach to rolling bearings faults based on kernek principle component analysis [J]. Journal of Central South University (Science and Technology), 2011, 42(11): 3384–3391. (in Chinese)Google Scholar
  3. [3]
    HU Chao, YOUN B D, WANG Ping-feng, TAEK Y Y. Ensemble of data-driven prongostic algorithms for robust prediction of remaining useful life [J]. Reliability Engineering and System Safety, 2012, 103: 120–135.CrossRefGoogle Scholar
  4. [4]
    TABRIZI A, GARIBALDI L, FASANA A, MARCHESIELLO S. Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine [J]. Meccanica, 2015; 50(3): 865–874.CrossRefGoogle Scholar
  5. [5]
    WANG Xia, LIU Cheng-wen, BI Feng-rong, BI Xiao-yang, SHAO Kang. Fault diagnosis of diesel engine based on adaptive wavelet packets and EEMD-fractal dimension [J]. Mechanical Systems and Signal Processing, 2013, 41: 581–597.CrossRefGoogle Scholar
  6. [6]
    WU Zhao-hua, HUANG NORDEN E. Ensemble empirical mode decomposition: A noise-assisted data analysis method [J]. Advances in Adaptive Data Analysis, 2009, 1: 1–41.CrossRefGoogle Scholar
  7. [7]
    ZHANG Chao, CHEN Jian-jun, GUO Xun. Gear fault diagnosis method based on ensemble empirical mode decomposition energy entropy and support vector machine [J]. Journal of Central South University (Science and Technology), 2012, 43(3): 932–939. (in Chinese)Google Scholar
  8. [8]
    LEI Ya-guo, HE Zheng-jia, ZI Yan-yang. EEMD method and WNN for fault diagnosis of locomotive roller bearings [J]. Expert Systems with Applications, 2011, 38: 7334–7341.CrossRefGoogle Scholar
  9. [9]
    OZGONENEL O, YALCIN T, GUNEY I, KURT U. A new classification for power quality events in distribution systems [J]. Electric Power Systems Research, 2013, 95: 192–199.CrossRefGoogle Scholar
  10. [10]
    SAIDI L, BEN A J, FRIAIECH F. Application of higher order spectral features and support vector machines for bearing faults classificati-on [J]. ISA Transactions, 2015, 54: 193–206.CrossRefGoogle Scholar
  11. [11]
    ZAREI J, MOHAMMAD T A, HAMID K R. Vibration analysis for bearing fault detection and classification using an intelligent filter [J]. Mechatronics, 2014, 24(2): 151–157.CrossRefGoogle Scholar
  12. [12]
    YU Gang, LI Chang-ning, SUN Jun. Machine fault diagnosis based on Gaussian mixture model and its application [J]. International Journal of Advanced Manufacturing Technology, 2010, 48(1-4): 205–212.CrossRefGoogle Scholar
  13. [13]
    HE Shu-ping, XU Hui-ling. Non-fragile finite-time filter design for time-delayed Markovian jumping systems via T-S fuzzy model approach [J]. Nonlinear Dynamics, 2015, 80(3): 1159–1171.CrossRefMATHGoogle Scholar
  14. [14]
    HE Shu-ping. Non-fragile passive controller design for nonlinear Markovian jumping systems via observer-based controls [J]. Neurocomputing, 2015, 147: 350–357.CrossRefGoogle Scholar
  15. [15]
    HUANG Run-qing, XI Li-feng, LI Xing-lin, LIU R C, QIU Hai, LEE J. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods [J]. Mechanical Systems and Signal Processing, 2007, 21: 193–207.CrossRefGoogle Scholar
  16. [16]
    CAI Wen, YANG Chun-yan. Basic theory and methodology on extenics [J]. Science China, 2013, 58(13): 1190–1199. (in Chinese)Google Scholar
  17. [17]
    CAI Wen, YANG Chun-yan, HE Bin. New development of the basic theory of extenics [J]. Engineering Science, 2003, 5(2): 80–87. (in Chinese)Google Scholar
  18. [18]
    LIU Yu-mei, ZHAO Cong-cong, XIONG Ming-ye, XU Wen-bin, ZHANG Zhi-yuan. Application of extension theory to monitoring the running state of the high-speed railway’s gearbox [J].Transactions of Beijing Institute of Technology, 2015, 35(11): 1135–1139. (in Chinese)Google Scholar
  19. [19]
    LIU Yu-mei, ZHAO Cong-cong, XIONG Ming-ye, ZHANG Zhi-yuan, QIAO Ning-guo. Optimization of classical domains for high-speed train transmission system [J]. Journal of Southwest Jiaotong University, 2016, 51(1): 85–90, 120. (in Chinese)Google Scholar
  20. [20]
    HE Yong-xiu, DAI Ai-ying, ZHU Jiang, HE Hai-ying, LI Fu-rong. Risk assessment of urban network planning in china based on the matter-element model and extension analysis [J]. International Journal of Electrical Power and Energy Systems, 2011, 33(3): 775–782.CrossRefGoogle Scholar
  21. [21]
    HUANG Po-hsian. The extenics theory for a matching evaluation system [J]. Computers and Mathematics with Applications, 2006, 52(6, 7): 997–1010.CrossRefGoogle Scholar
  22. [22]
    WANG Chuan-fei, AN Gang, YANG Fan-jie. Research on fault diagnosis of certain armoured vehicle’ Gear-Box with IMF’s energy moment [J]. Advanced Materials Research, 2011, 383–390: 248–253.CrossRefGoogle Scholar
  23. [23]
    QIU Hai, LEE Jay, LIN Jing, YU Gang. Wavelet filter-based weak signature detection method and its application on roller bearing prognostics [J]. Journal of Sound and Vibration, 2006, 289(4, 5): 1066–1090.CrossRefGoogle Scholar

Copyright information

© Central South University Press and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Yu-mei Liu (刘玉梅)
    • 1
  • Cong-cong Zhao (赵聪聪)
    • 2
  • Ming-ye Xiong (熊明烨)
    • 3
  • Ying-hui Zhao (赵颖慧)
    • 4
  • Ning-guo Qiao (乔宁国)
    • 1
  • Guang-dong Tian (田广东)
    • 1
  1. 1.Transportation CollegeJilin UniversityChangchunChina
  2. 2.College of Engineering and TechnologyJilin Agricultural UniversityChangchunChina
  3. 3.University of Illinois at Urbana-ChampaignIllinoisUSA
  4. 4.Engineering DepartmentChangchun Visteon Faway Automotive Electronics Co., Ltd.ChangchunChina

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