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Journal of Mechanical Science and Technology

, Volume 33, Issue 1, pp 129–138 | Cite as

Sharing pattern feature selection using multiple improved genetic algorithms and its application in bearing fault diagnosis

  • Xiaoying Guan
  • Guo ChenEmail author
Article
  • 5 Downloads

Abstract

In order to select the effective features or feature subsets and realize an intelligent diagnosis of aero engine rolling bearing faults, this paper presents a sharing pattern feature selection method using multiple improved genetic algorithms. Based on the simple genetic algorithm, a multiple-population improved genetic algorithm was proposed, which improves the speed and effect of algorithm and overcomes the shortcomings of local optima that simple genetic algorithm is easy to fall into. Because all populations regularly share and exchange their selecting features, the proposed algorithms can quickly dig up the current effective feature patterns, and then analyze and deal with the strong correlation between the feature patterns. This will not only give clear directions for the descendant evolution, but also help to achieve high accuracy feature selection, for, the features are highly distinctive. This multiple-population improved genetic algorithm was applied to rolling bearing fault feature selection and comparisons with other methods are carried out, which demonstrates the validity of sharing pattern feature selection method proposed.

Keywords

Feature selection Feature pattern Multiple-population Genetic algorithm Bearing Fault diagnosis 

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References

  1. [1]
    O. Gustafsson and T. Tallian, Detection of damage in assembled rolling element bearings, A S L E Transactions, 5 (1) (1962) 197–209.CrossRefGoogle Scholar
  2. [2]
    P. G. Wheeler, Bearing analysis keeps downtime down, Plant Engineering, 25 (1968) 87–89.Google Scholar
  3. [3]
    R. Martin, Detection of ball bearing malfunctions, Instruments and Control Systems, 12 (1970) 79–82.Google Scholar
  4. [4]
    X. Zhao and B. Ye, Study on clear extraction of amplitude spectrum of vibrating signal for a rolling bearing by art-2 and its classification effect, Journal of Vibration & Shock, 26 (1) (2007) 139–143+150.MathSciNetGoogle Scholar
  5. [5]
    B. E. Parker Jr. et al., Diagnostics using statistical change detection in the bispectrum domain, Mechanical Systems and Signal Processing, 14 (4) (2000) 561–570.MathSciNetCrossRefGoogle Scholar
  6. [6]
    Z. Y. Rui, L. Y. Xu and G. M. Li, Fault detection of roller bearing based on cepstrum, Bearing, 1 (2007) 35–37.Google Scholar
  7. [7]
    J. T. Yang, J. J. Chen and Z. P. Zeng, Extracting fault features using higher order spectra for rotating machinery, Journal of Vibration Engineering, 14 (1) (2001) 13–17.Google Scholar
  8. [8]
    P. W. Tse, Y. H. Peng, R. Yam and R. Yam, Wavelet analysis and envelope detection for rolling element bearing fault diagnosis-their effectiveness and flexibilities, Journal of Vibration & Acoustics, 123 (3) (2001) 303–310.CrossRefGoogle Scholar
  9. [9]
    G. Chen, Feature extraction and intelligent diagnosis for ball bearing early faults, Acta Aeronautica ET Astronautica Sinica, 30 (2) (2009) 362–367.Google Scholar
  10. [10]
    J. S. Cheng, D. J. Yu and Y. Yang, Fault diagnosis of roller bearings based on EM D and SVM, Journal of Aerospace Power, 21 (3) (2006) 575–580.Google Scholar
  11. [11]
    J. Antoni and R. B. Randall, The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines, Mechanical Systems and Signal Processing, 20 (2006) 308–331.CrossRefGoogle Scholar
  12. [12]
    F. Cong et al., Short-time matrix series based singular value decomposition for rolling bearing fault diagnosis, Mechanical Systems & Signal Processing, 34 (1–2) (2013) 218–230.CrossRefGoogle Scholar
  13. [13]
    A. Akram, N. Rami and A. A. Ahmed, Enhancing the diversity of genetic algorithm for improved feature selection, 2010 IEEE International Conference on Systems Man and Cybernetics (SMC), Piscataway, NJ: IEEE Press (2011) 1325–1331.Google Scholar
  14. [14]
    Q. W. Yang et al., Improving genetic algorithms by using logic operation, Control and Decision, 15 (4) (2000) 510–512.Google Scholar
  15. [15]
    Y. Li, S. Zhang and X. Zeng, Research of multi-population agent genetic algorithm for feature selection, Expert Systems with Applications, 36 (9) (2009) 11570–11581.CrossRefGoogle Scholar
  16. [16]
    D. Dutta, P. Dutta and J. Sil, Simultaneous feature selection and clustering for categorical features using multi objective genetic algorithm, International Conference on Hybrid Intelligent Systems, IEEE (2012) 191–196.Google Scholar
  17. [17]
    P. Chen, T. Toyota and Z. He, Automated function generation of symptom parameters and application to fault diagnosis of machinery under variable operating conditions, Systems Man & Cybernetics Part A Systems & Humans IEEE Transaction on, 31 (6) (2001) 775–781.CrossRefGoogle Scholar
  18. [18]
    M. Kang et al., Time-varying and multi-resolution envelope analysis and discriminative feature analysis for bearing fault diagnosis, Industrial Electronics, IEEE Transactions on, 62 (12) (2015) 7749–7761.Google Scholar
  19. [19]
    M. M. Ettefagh, M. Ghaemi and M. Y. Asr, Bearing fault diagnosis using hybrid genetic algorithm K-means clustering, Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on. IEEE (2014) 84–89.Google Scholar
  20. [20]
    M. Mitchell, An introduction to genetic algorithms, Cambridge, MA: MIT Press (1996) 2, ISBN 9780585030944.Google Scholar
  21. [21]
    I. Kononenko, Estimation attributes: Analysis and extensions of ReliefF, Proceedings of the 1994 European Conference on Machine Learning, Catania, Italy: Springer Verlag (1994) 171–182.Google Scholar
  22. [22]
    L. X. Zhang et al., Combination feature selection based on Relief, Journal of Fudan University (Natural Science), 43 (5) (2004) 893–898.MathSciNetGoogle Scholar
  23. [23]
    K. Pearson, Note on regression and inheritance in the case of two parents, Proceedings of the Royal Society of London, 58 (2006) 240–242.Google Scholar
  24. [24]
    X. G. Zhang, Pattern recognition, Third edition, Beijing: Tsinghua University Press (2010) 146–148.Google Scholar
  25. [25]
    X. Y. Guan, G. Chen and T. Lin, Feature selection method based on differential evolution and genetic algorithm with multi-criteria evaluation and its applications, Acta Aeronautica et Astronautica Sinica, 37 (11) (2016) 3455–3465.Google Scholar
  26. [26]
    G. Chen et al., Sensitivity analysis of fault diagnosis of aeroengine rolling bearing based on vibration signal measured on casing, Journal of Aerospace Power, 29 (12) (2014) 2874–2884.Google Scholar
  27. [27]
    H. B. Mei, Rolling bearing vibration monitoring and diagnosis, Beijing: China Machine Press (1995).Google Scholar
  28. [28]
    I. H. Witten, E. Frank and M. A. Hall, Data mining: Practical machine learning tools and techniques, Third Edition, Beijing: China Machine Press (2005).zbMATHGoogle Scholar
  29. [29]
    L. Yu and H. Liu, Efficient feature selection via analysis of relevance and redundancy, Journal of Machine Learning Research, 5 (2004) 1205–1224.MathSciNetzbMATHGoogle Scholar
  30. [30]
    M. A. Hall, Correlation-based feature selection for discrete and numeric class machine learning, Seventeenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc (2000) 359–366.Google Scholar

Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Civil AviationNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.School of SoftwareGuangdong Food and Drug Vocational CollegeGuangzhouChina

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