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.
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References
O. Gustafsson and T. Tallian, Detection of damage in assembled rolling element bearings, A S L E Transactions, 5 (1) (1962) 197–209.
P. G. Wheeler, Bearing analysis keeps downtime down, Plant Engineering, 25 (1968) 87–89.
R. Martin, Detection of ball bearing malfunctions, Instruments and Control Systems, 12 (1970) 79–82.
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.
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.
Z. Y. Rui, L. Y. Xu and G. M. Li, Fault detection of roller bearing based on cepstrum, Bearing, 1 (2007) 35–37.
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.
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.
G. Chen, Feature extraction and intelligent diagnosis for ball bearing early faults, Acta Aeronautica ET Astronautica Sinica, 30 (2) (2009) 362–367.
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.
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.
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.
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.
Q. W. Yang et al., Improving genetic algorithms by using logic operation, Control and Decision, 15 (4) (2000) 510–512.
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.
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.
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.
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.
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.
M. Mitchell, An introduction to genetic algorithms, Cambridge, MA: MIT Press (1996) 2, ISBN 9780585030944.
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.
L. X. Zhang et al., Combination feature selection based on Relief, Journal of Fudan University (Natural Science), 43 (5) (2004) 893–898.
K. Pearson, Note on regression and inheritance in the case of two parents, Proceedings of the Royal Society of London, 58 (2006) 240–242.
X. G. Zhang, Pattern recognition, Third edition, Beijing: Tsinghua University Press (2010) 146–148.
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.
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.
H. B. Mei, Rolling bearing vibration monitoring and diagnosis, Beijing: China Machine Press (1995).
I. H. Witten, E. Frank and M. A. Hall, Data mining: Practical machine learning tools and techniques, Third Edition, Beijing: China Machine Press (2005).
L. Yu and H. Liu, Efficient feature selection via analysis of relevance and redundancy, Journal of Machine Learning Research, 5 (2004) 1205–1224.
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.
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Recommended by Associate Editor Gyuhae Park
X. Y. Guan received a master degree in the School of Software from the Sun Yat-sen University, Guangzhou, P. R. China, in 2008. Now she is a student in the College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China. Her current research interests include genetic algorithm, pattern recognition and machine learning, and their application in bearing fault diagnosis.
G. Chen received a Ph.D. degree in the School of Mechanical Engineering from the Southwest Jiaotong University, Chengdu, P. R. China, in 2000. Now he works at the College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China. His current research interests include the whole aero-engine vibration, rotor-bearing dynamics, rotating-machine fault diagnosis, pattern recognition and machine learning, signal analysis and processing.
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Guan, X., Chen, G. Sharing pattern feature selection using multiple improved genetic algorithms and its application in bearing fault diagnosis. J Mech Sci Technol 33, 129–138 (2019). https://doi.org/10.1007/s12206-018-1213-6
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DOI: https://doi.org/10.1007/s12206-018-1213-6