Abstract
The feature extraction problem of coupled vibration signals with multiple fault modes of planetary gear has not been solved effectively. At present, kernel principal component analysis (KPCA) is usually used for nonlinear feature extraction, but the blind setting of kernel function parameters greatly affects the performance of KPCA algorithm. For the optimization of kernel parameters, it is necessary to study theoretical modeling to improve KPCA performance. In this paper, employing a Fisher criterion (FC) discriminant function in pattern recognition, the optimization mathematical model of the kernel parameter was presented and the improved particle swarm optimization algorithm (PSO) was applied to search for the optimum value, and the performance of the Kernel principal component analysis for nonlinear problems was improved. The optimized KPCA was applied for feature extraction of different wear fault modes of a planetary gear, and the feature dimensions were reduced from 27 to 10. The feature parameters with 92.9 % contribution rates were retained and sample sets were formed to feed the support vector machine (SVM) for final classification and identification. The intelligently optimized KPCA based on the PSO-FC has improved the structural distribution of data in the feature space and showed a good scale clustering effect in planetary gear wear state recognition. The accuracy of the SVM classification was improved by 17.5 %.
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References
C. Sun and Y. Wang, Advance in study of fault diagnosis of helicopter planetary gears, Acta Aeronaut. Astronaut. Sin., 37(7) (2017) 1–13.
Y. Lei et al., Condition monitoring and fault diagnosis of planetary gearboxes: a review, Measurement, 48(1) (2014) 292–305.
X. Liang et al., Dynamic modeling of gearbox faults: a review, Mech. Syst. Signal Process, 98 (2018) 852–876.
Lei et al., A new dynamic model of planetary gear sets and research on fault response characteristics, J. Mech. Eng., 52(13) (2016) 111–122.
J. Wu et al., Fault feature analysis of cracked gear based on LOD and analytical-FE method, Mech. Syst. Signal Process., 98 (2018) 951–967.
X. Liu et al., Resultant vibration signal model based fault diagnosis of a single stage planetary gear train with an incipient tooth crack on the sun gear, Renew. Energy, 122 (2018) 65–79.
G. Cheng et al., Study on planetary gear fault diagnosis based on entropy feature fusion of ensemble empirical mode decomposi-tion, Measurement, 91 (2016) 140–154.
X. Chen and Z. Feng, Iterative generalized time-frequency reassignment for planetary gearbox fault diagnosis under nonstationary conditions, Mech. Syst. Signal Process., 80 (2016) 429–444.
Y. Lei et al., Health condition identification of multi-stage planetary gearboxes using a mRVM-based method, Mech. Syst. Signal Process, 60–61 (2015) 289–300.
M. Khazaee et al., An appropriate approach for condition monitoring of planetary gearbox based on fast fourier transform and least-square support vector machine, Int. J. Multidiscip. Sci. Eng., 3 (2012) 22–26.
Z. Liu et al., A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT, KPCA and Twin SVM Testing twin SVM, ISA Trans, 66 (2017) 249–261.
Z. Liu et al., Fault level diagnosis for planetary gearboxes using hybrid kernel feature selection and kernel Fisher discriminant analysis, Int. J. Adv. Manuf. Technol., 67(5–8) (2013) 1217–1230.
H. X. Vo and L. J. Durlofsky, Regularized kernel PCA for the efficient parameterization of complex geological models, J. Comput. Phys., 322 (2016) 859–881.
R. Liu et al., Artificial intelligence for fault diagnosis of rotating machinery: a review, Mech. Syst. Signal Process., 108 (2018) 33–47.
S. Qing et al., Point cloud simplification algorithm based on particle swarm optimization for online measurement of stored bulk grain, Int. J. Agric. Biol. Eng., 9(1) (2016) 71–78.
M. Kallas et al., Fault detection and estimation using kernel principal component analysis, IFAC-PapersOnLine, 50(1) (2017) 1025–1030.
X. Wei, Study on Inteliigent Fauit Diagnosis of Gearbox Based on Particie Swarm Optimization, North University of China (2009).
X. Deng and L. Wang, Modified kernel principal component analysis using double-weighted local outlier factor and its application to nonlinear process monitoring, ISA Trans., 72 (2018) 218–228.
D. Kapsoulis et al., Evolutionary multi-objective optimization assisted by metamodels, kernel PCA and multi-criteria decision making techniques with applications in aerodynamics, Appl. Soft Comput. J, 64 (2018) 1–13.
J. Saari et al., Detection and identification of windmill bearing faults using a one-class support vector machine (SVM), Measurement, 137 (2019) 287–301.
Z. He et al., Support tensor machine with dynamic penalty factors and its application to the fault diagnosis of rotating machinery with unbalanced data, Mech. Syst. Signal Process., 141 (2020) 106441.
M. Zhang et al., An improved sideband energy ratio for fault diagnosis of planetary gearboxes, J. Sound Vib., 491 (2021) 115712.
H. Yan and W. ZongYan, Fault diagnosis of planetary gearbox based on SFLA-BP model and KPCA feature extraction, J. Mech. Strength, 42(2) (2020) 263–269.
Y. He and Z. Wang, Regularized kernel function parameter of kpca using wpso-fda for feature extraction and fault recognition of gearbox, J. Vibroengineering, 20(1) (2018) 225–239.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant Nos. 52005455), the Shanxi Province Science Foundation for Youths (Grants Nos. 201901 D211205 and 201901D211201), and the Opening Project of Shanxi Key Laboratory of Advanced Manufacturing Technology (Grants Nos. XJZZ202002).
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Linzheng Ye is an Associate Professor of Mechanical Engineering, North University of China, Taiyuan, China. He received his Ph.D. in Mechanical Engineering from North University of China. His research interests include precision and special machining, fault diagnosis and ultrasonics cavitation.
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He, Y., Ye, L., Zhu, X. et al. Feature extraction based on PSO-FC optimizing KPCA and wear fault identification of planetary gear. J Mech Sci Technol 35, 2347–2357 (2021). https://doi.org/10.1007/s12206-021-0507-2
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DOI: https://doi.org/10.1007/s12206-021-0507-2