Study on Coupling Faults Diagnosis of Gear Rotor System

Abstract:

Coupling faults diagnose methods of gear rotor system are studied. Moment invariants are extracted from axis orbit of rotor system, which used to identify imbalances, loosening faults. Mode functions with different characteristic time scales are obtained based on the EMD method. Marginal spectrum is obtained by Hilbert transform, which can used to identify the wear faults of gear system. Moment invariant and BP neural network are used to identify the unbalanced fault and loosening faults. Hilbert transform method is used to diagnose gear wear fault. Experiments prove the effective of the methods.

• Periodical: Applied Mechanics and Materials (Volume 281)
• Pages: 374-377
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.281.374
• Online since: January 2013
• Authors: Li Cui, Da Fang Shi, Jian Rong Zheng, Xiao Guang Song
• Keywords: Axis Orbit, Coupling Faults, Diagnosis, Gear Rotor System, Hilbert Transform

References

[1] Wood J. Invariant pattern recognition: a review[J]. Pattern Recognition. 1996, 29(1): 1-17.

[2] Cai Wan, Chang Jian. Strong nonlinearity analysis for gear-bearing system under nonlinear suspension bifurcation and chaos[J]. Nonlinear Analysis: Real World Applications．2010, 11: 1760-1774.

DOI: 10.1016/j.nonrwa.2009.03.027

[3] Shi D F, Wang W J, Unsworth P J, et al. Purification and feature extraction of shaft orbits for diagnosing large rotating machinery[J]. Journal of Sound and Vibration. 2005(279): 581-600.

DOI: 10.1016/j.jsv.2003.11.036

[4] Peng Z, He Y, Chen Z, et a1. Identification of the shaft orbit for rotating machines using wavelet modulus maxima[J]. Mechanical Systems and Signal Processing. 2002, 16(4): 623-635.

DOI: 10.1006/mssp.2002.1494

[5] Miroslav Byrtus, Vladimír Zeman. On modeling and vibration of gear drives influenced by nonlinear couplings[J]．Mechanism and Machine Theory. 2011, 46: 375-397.

DOI: 10.1016/j.mechmachtheory.2010.10.007

[6] Li Hui, Zhang Yuping, Zheng Haiqi. Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings[J]. Journal of Mechanical Science and Technology. 2009, 23(2): 291-301.

DOI: 10.1007/s12206-008-1110-5

[7] Wu Zhaohua, Huang Norden E. Ensemble empirical mode Decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis. 2009, (1): 1-4.

DOI: 10.1142/s1793536909000047

[8] A. Parey. M. El Badaoui. F. Guillet. Dynamic modelling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect. Journal of Sound and Vibration 2006, 294: 547-561.

DOI: 10.1016/j.jsv.2005.11.021

[9] Yvon Gauthier． Hilbet's idea of a physical axiomatics：the analytical apparatus of quantum mechanics．Journal of Physical Mathematics. (2010).

[10] Yan R.Q., Gao R. A tour of the Hilbert-Huang transform: aH empirical tool for signal analysis．IEEE Instrumentatl on＆Measurement Magazine. 2007, 10(5): 223-231.