Aeroengine Rub-Impact Fault Diagnosis Based on Wavelet Packet Transform and the Local Discriminate Bases


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With the development of aeroengine towards the direction of high speed and high performance, the clearance between rotor and stator in aerongine is reduced so that the possibility of rub-impact fault is increased. Since rub-impact signals often exhibits non-stationarity, an integrated approach, which combines the wavelet packet transform (WPT) with local discriminate bases (LDB), is presented in this study to diagnose the rub-impact faults. Specifically, the LDB algorithm is used to select an optimal set of orthogonal time-frequency subspaces resulted from WPT, which have the best discriminatory information for aeroengine rub-impact fault classification. Then the desired parameters generated by the LDB vectors were taken as input to a Bayes classifier for identifying rub-impact faults. Experimental results from the aeroengine vibration signals show that the fault diagnosis method can classify working conditions and fault patterns effectively.



Edited by:

Chunliang Zhang and Paul P. Lin




Y. H. Wu et al., "Aeroengine Rub-Impact Fault Diagnosis Based on Wavelet Packet Transform and the Local Discriminate Bases", Applied Mechanics and Materials, Vols. 226-228, pp. 740-744, 2012

Online since:

November 2012




[1] Y.H. Wu, J.F. Xue, D.Z. Zhang and X.L. Li: Proc. of the 2010 International Conference on Wavelet Analysis and Pattern Recognition (Qingdao, China, July 11-14, 2010). Vol. 10, p.421.

[2] H.L. Zhang, J.M. Zhou and G. Li: Proceedings of the CSEE, Vol. 26 (2006) No. 8, p.159(in Chinese).

[3] F.L. Zhu et al: Chinese Journal of Mechanical Engineering, Vol. 38 (2002) No. 3, p.139 (in Chinese).

[4] W.J. Fu et al: Journal of Shenyang Institute of Aeronautical Engineering, Vol. 25 (2008) No. 3, p.11 (in Chinese).

[5] Q.B. He,R.Q. YAN and Robert X. Gao: Prognostics and System Health Management Conference(Macau, China, January 12-14, 2010)Vol. 1, p.1.

[6] S. Mallat: A Wavelet Tour of Signal Processing (Academic Press, Canada 1999).

[7] C. Marwa, K. Mohamad and D. Jacques: Signal Processing, vol. 86 (2006) No. 16, p.3826.

[8] Y. Ma et al: Technical Acoustics, Vol. 23 (2004) No. 21, p.117(in Chinese).

[9] N. Saito and R.R. Coifmann: Journal of Mathematical Imaging and Vision, vol. 5 (1995) No. 4, p.337.

[10] K. Umapathy, S. Krishnan, R.K. Rao: IEEE Transactions on Audio Speech and Language Processing, Vol. 15 (2007) No. 4, p.1236.