An Enhanced CICA Method and Its Application to Multistage Gearbox Low-frequency Fault Feature Extraction

Author(s): Junfa Leng*, Penghui Shi, Shuangxi Jing, Chenxu Luo

Journal Name: Recent Advances in Electrical & Electronic Engineering
Formerly Recent Patents on Electrical & Electronic Engineering

Volume 13 , Issue 2 , 2020


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Graphical Abstract:


Abstract:

Background: The vibration signals acquired from multistage gearbox’s slow-speed gear with localized fault may be directly mixed with source noise and measured noise. In addition, Constrained Independent Component Analysis (CICA) method has strong immunity to the measured noise but not to the source noise. These questions cause the difficulty for applying CICA method to directly extract lowfrequency and weak fault characteristic from the gear vibration signals with source noise.

Methods: In order to extract the low-frequency and weak fault feature from the multistage gearbox, the source noise and measured noise are introduced into the independent component analysis (ICA) algorithm model, and then an enhanced Constrained Independent Component Analysis (CICA) method is proposed. The proposed method is implemented by combining the traditional Wavelet Transform (WT) with Constrained Independent Component Analysis (CICA).

Results: In this method, the role of a supplementary step of WT before CICA analysis is explored to effectively reduce the influence of strong noise.

Conclusion: Through the simulations and experiments, the results show that the proposed method can effectively decrease noise and enhance feature extraction effect of CICA method, and extract the desired gear fault feature, especially the low-frequency and weak fault feature.

Keywords: Multistage gearbox, feature extraction, wavelet transform (WT), independent component analysis (ICA), constrained independent component analysis (CICA), Noisy ICA model.

[1]
Y. Lei, Z. Liu, and X. Wu, "Health condition identification of multi-stage planetary gearboxes using a mRVM-based method", Mech. Syst. Signal Process., vol. 60-61, pp. 289-300, 2015.
[2]
J. Igba, K. Alemzadeh, and C. Durugbo, "Analyzing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes", Renew. Energy, vol. 3, pp. 90-106, 2016.
[3]
V. Skrickij, M. Bogdevicius, and R. Junevicius, "Diagnostic features for the condition monitoring of hypoid gear utilizing the wavelet transform", Appl. Acoust., vol. 106, pp. 51-62, 2016.
[4]
J. Chen, C. Zhang, and X. Zhang, "Planetary gearbox condition monitoring of ship-based satellite communication antennas using ensemble multiwavelet analysis method", Mechan. Syst. and Sig. Process., vol. 54-55, pp. 277-292, 2015.
[5]
X-H. Chen, G. Cheng, and X-L. Shan, "Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance", Measure., vol. 73, pp. 55-67, 2015.
[6]
T. Wang, F. Chu, and Q. Han, "Compound faults detection in gearbox via meshing resonance and spectral kurtosis methods", J. Sound Vibrat., vol. 392, pp. 367-381, 2017.
[7]
S. Aouabdi, M. Taibi, and S. Bouras, "Using multi-scale entropy and principal component analysis to monitor gears degradation via the motor current signature analysis", Mech. Syst. Signal Process., vol. 90, pp. 298-316, 2017.
[8]
Z. Feng, Y. Zhou, and M.J. Zuo, "Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: A review with examples", Measurement, vol. 103, pp. 106-132, 2017.
[9]
G.L. McDonald, Q. Zhao, and M.J. Zuo, "Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection", Mech. Syst. Signal Process., vol. 33, pp. 237-255, 2012.
[10]
G.L. McDonald, and Q. Zhao, "Multipoint optimal minimum entropy deconvolution and convolution fix: Application to vibration fault detection", Mech. Syst. Signal Process., vol. 82, pp. 461-477, 2017.
[11]
A. Hyvärinen, "Fast and robust fixed-point algorithms for independent component analysis", IEEE Trans. Neural Netw., vol. 10, pp. 626-634, 1999.
[12]
P. Comon, and C. Jutten, Handbook of Blind Source Separation: Independent Component Analysis and Application., Burlington: Academic Press, 2010.
[13]
Y. Guo, J. Na, and B. Li, "Envelope extraction based dimension reduction for independent component analysis in fault diagnosis of rolling element bearing", J. Sound Vibrat., vol. 333, pp. 2983-2994, 2014.
[14]
M. Žvokelj, S. Zupan, and I. Prebil, "EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis", J. Sound Vibrat., vol. 370, pp. 394-423, 2016.
[15]
W. Lu, and J.C. Rajapakse, "ICA with reference", Neuro-Comput., vol. 69, pp. 2244-2257, 2006.
[16]
Z-L. Zhang, "Morphologically constrained ICA for extracting weak temporally correlated signals", Neurocomput., vol. 71, pp. 1669-1679, 2008.
[17]
X. Wang, Z. Huang, and Y. Zhou, "Approaches and applications of semi-blind signal extraction for communication signals based on constrained independent component analysis: The complex case", Neurocomput., vol. 101, pp. 204-216, 2013.
[18]
Z. Wang, J. Chen, and G. Dong, "Constrained independent component analysis and its application to machine fault diagnosis", Mech. Syst. Signal Process., vol. 25, pp. 2501-2512, 2011.
[19]
"Z.-Yang Wang, J. Chen and W.-B. Xiao, “Fault diagnosis of rolling element bearing based on constrained independent component analysis", J. Vib. Shock, vol. 31, pp. 118-122, 2012.
[20]
T. Yang, Y. Guo, and X. Wu, "Fault feature extraction based on combination of envelope order tracking and CICA for rolling element bearings", Mech. Syst. Signal Process., vol. 113, pp. 131-144, 2018.
[21]
N. Ahamed, Y. Pandya, and A. Parey, "Spur gear tooth root crack detection using time synchronous averaging under fluctuating speed", Measurement, vol. 52, pp. 1-11, 2014.
[22]
B. Liang, S. Iwnicki, and A. Ball, "Adaptive noise canceling and time-frequency techniques for rail surface defect detection", Mech. Syst. Signal Process., vol. 54-55, pp. 41-51, 2015.
[23]
C. Mishra, A.K. Samantaray, and G. Chakraborty, "Rolling element bearing fault diagnosis under slow speed operation using wavelet de-noising", Measurement, vol. 103, pp. 77-86, 2017.
[24]
J. Chen, Z. Yanyang, and Z. He, "Adaptive redundant multiwavelet denoising with improved neighboring coefficients for gearbox fault detection", Mech. Syst. Signal Process., vol. 38, pp. 549-568, 2013.
[25]
J.F. Leng, S.X. Jing, and C.X. Luo, "EEMD-Based CICA method for single-channel signal separation and fault feature extraction of gearbox", J. Vibroeng., vol. 19, pp. 5858-5873, 2017.
[26]
Z. Feng, and M.J. Zuo, "Vibration signal models for fault diagnosis of planetary gearboxes", J. Sound Vibrat., vol. 333, pp. 4919-4939, 2012.
[27]
Q. Cheng, H. Zhou, and J. Cheng, "The fisher-markov selector: Fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data", IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, pp. 1217-1233, 2011.


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Article Details

VOLUME: 13
ISSUE: 2
Year: 2020
Published on: 26 April, 2020
Page: [285 - 294]
Pages: 10
DOI: 10.2174/2352096512666190130100336
Price: $25

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