Abstract
Aiming at the problem of gear fault feature extraction and fault classification under different load excitation, we present a new fault diagnosis method that combines three methods, including empirical mode decomposition (EMD), particle swarm optimization support vector machine (PSO-SVM) and fractal box dimension. First, the non-stationary original vibration signal of gear fault is decomposed into several intrinsic mode functions (IMF) by EMD method. Then, the time, frequency, energy characteristic parameters and box dimension are calculated separately from the time domain, frequency domain, energy domain and fractal domain. And then the gear fault characteristics under different load excitation are obtained. Finally, the extracted feature parameters are input into the PSO-SVM model for gear fault classification. The experimental results show that the proposed method can effectively identify gear failure types under different load excitation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
D. L. Yang, Y. L. Liu, S. B. Li, X. J. Li and L. Y. Ma, Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm, Mechanism and Machine Theory, 90 (2015) 219–229.
R. Ma, Y. S. Chen and Q. J. Cao, Research on dynamics and fault mechanism of spur gear pair with spalling defect, J. of Sound and Vibration, 331 (9) (2012) 2097–2109.
W. Wang and P. McFadden, Application of wavelets to gear box vibration signals for fault detection, J. of Sound and Vibration, 192 (5) (1996) 927–939.
H. Liu and J. S. Dhupia, A time domain approach to diagnose gearbox fault based on measured vibration signals, J. of Sound and Vibration, 333 (7) (2014) 2164–2180.
D. Zhang, D. Yu and W. Zhang, Energy operator demodulating of optimal resonance components for the compound faults diagnosis of gearboxes, Measurement Science and Technology, 26 (11) (2015) 115003.
C. Li, R. V. Sanchez, G. Zurita, M. Cerrada, D. Cabrera and R. E. Vasquezc, Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis, Neurocomputing, 168 (C) (2015) 119–127.
Z. Q. Xing, J. F. Qu, Y. Chai, Q. Tang and Y. M. Zhou, Gear fault diagnosis under variable conditions with intrinsic timescale decomposition–singular value decomposition and support vector machine, J. of Mechanical Science and Technology, 31 (2) (2017) 545–553.
X. H. Chen, G Cheng, H. Y. Li and Y. Li, Research of planetary gear fault diagnosis based on multi–scale fractal box dimension of CEEMD and ELM, J. of Mechanical Engineering, 63 (2017) 45–55.
H. Ma, X. Pang, R. J. Feng, R. Z. Song and B. C. Wen, Fault features analysis of cracked gear considering the effects of the extended tooth contact, Engineering Failure Analysis, 48 (2015) 105–120.
R. Jiang, J. Yu and V. Makis, Optimal Bayesian estimation and control scheme for gear shaft fault detection, Comput. Ind. Eng., 63 (4) (2012) 754–762.
X. H. Chen, G. Cheng, H. Y. Li and M. Zhang, Diagnosing planetary gear faults using the fuzzy entropy of LMD and ANFIS, J. of Mechanical Science and Technology, 30 (6) (2016) 2453–2462.
W. Cai and J. Chang, Strong nonlinearity analysis for gearbearing system under nonlinear suspension–bifurcation and chaos, Nonlinear Analysis: Real World Applications, 11 (3) (2010) 1760–1774.
Z. G. Chen and Y. M. Shao, Dynamic simulation of spur gear with tooth root crack propagating along tooth width and crack depth, Engineering Failure Analysis, 18 (8) (2011) 2149–2164.
Y. Yang, Y. G. He, J. S. Cheng and D. J. Yu, A gear fault diagnosis using Hilbert spectrum based on MODWPT and a comparison with EMD approach, Measurement, 42 (4) (2009) 542–551.
P. M. Shi, C. J. Su and D. Y. Han, Fault diagnosis of rotating machinery based on adaptive stochastic resonance and AMD–EEMD, Shock and Vibration (2016) 9278581
P. M. Shi, S. J. An, P. Li and D. Y. Han, Signal feature extraction based on cascaded multi–stable stochastic resonance denoising and EMD method, Measurement, 90 (2016) 318–328.
D. Y. Han, P. Li, S. J. An and P. M. Shi, Multi–frequency weak signal detection based on wavelet transform and parameter compensation band–pass multi–stable stochastic resonance, Mechanical Systems and Signal Processing, 70–71 (2016) 995–1010.
Y. Liu, J. L. Lin and K. Chen, A stable algorithm of box fractal dimension and its application in pore structure, Rare Metal Materials and Engineering, 44 (4) (2015) 800–804.
J. Y. Yang, Y. Y. Zhang and Y. S. Zhu, Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension, Mechanical Systems and Signal Processing, 21 (5) (2007) 2012–2024.
M. Khazaee, H. Ahmadi, M. Omid, A. Banakar and A. Moosavian, Feature–level fusion based on wavelet transform and artificial neural network for fault diagnosis of planetary gearbox using acoustic and vibration signals, Insight, 55 (6) (2013) 323–329.
J. Kennedy and R. C. Eberhart, Particle swarm optimization, Proceedings of IEEE International Conference on Neural Networks, IEEE: Piscataway (1995) 1942–1948.
Z. W. Liu, H. R. Cao, X. F. Chen, Z. J. He and Z. J. Shen, Multi–fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings, Neurocomputing, 99 (1) (2013) 399–410.
K. H. Zhu, X. G. Song and D. G. Xue, A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm. Measurement, 47 (1) (2014) 669–675.
Author information
Authors and Affiliations
Corresponding author
Additional information
Recommended by Associate Editor Kyoung-Su Park
Dongying Han received her Ph.D. from the Mechanical Engineering Institute of Yanshan university, Qinhuangdao, China, in 2008. She is an Associate Professor in the Institute of Vehicles and Energy of Yanshan University. Her current research interests include fault diagnosis and signal processing.
Rights and permissions
About this article
Cite this article
Han, D., Zhao, N. & Shi, P. Gear fault feature extraction and diagnosis method under different load excitation based on EMD, PSO-SVM and fractal box dimension. J Mech Sci Technol 33, 487–494 (2019). https://doi.org/10.1007/s12206-019-0101-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12206-019-0101-z