A Multi-Fault Diagnosis Method of Rolling Bearing Based on Wavelet-PCA and Fuzzy K-Nearest Neighbor

Abstract:

Rolling bearings are widely used in various areas including aircraft, mining, manufacturing, and agriculture, etc. The breakdowns of the rotational machinery resulted from the rolling bearing failures account for 30%. It is therefore imperative to monitor the rolling bearing conditions in time in order to prevent the malfunctions of the plants. In the present paper is described a fault detection and diagnosis technique for rolling bearing multi-faults based on wavelet-principle component analysis (PCA) and fuzzy k-nearest neighbor (FKNN). In the diagnosis process, the wavelet analysis was firstly employed to decompose the vibration data of the rolling bearings under eight different operating conditions, and for each sample its energy of each sub-band was calculated to obtain the original feature space. Then, the PCA was used to reduce the dimensionality of the original feature vector and hence the most important features could be gotten. Lastly, the FKNN algorithm was employed in the pattern recognition to identify the conditions of the bearings of interest. The experimental results suggest that the sensitive fault features can be extracted efficiently after the wavelet-PCA processing, and the proposed diagnostic system is effective for the rolling bearing multi-fault diagnosis. In addition, the proposed method can achieve higher performance than that without PCA with respect to the classification rate.

• Periodical: Applied Mechanics and Materials (Volumes 29-32)
• Pages: 1602-1607
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.29-32.1602
• Online since: August 2010
• Authors: Xiang Shun Chen, Hu Biao Zeng, Zhi Xiong Li
• Keywords: Fuzzy K-Nearest Neighbor, Multi-Fault Diagnosis, Principal Component Analysis (PCA), Rolling Bearing

References

[1] H. Yu, Q. Guo, J. Hu, et al.: Hybrid PSO based wavelet neural networks for intelligent fault diagnosis. Springer LNCS, Vol. 4221 (2007), pp.321-330.

[2] Y. Yu, D. Yu and J. Cheng: A roller bearing fault diagnosis method based on EMD energy entropy and ANN, Journal of sound and vibration, Vol. 294 (2006), pp.269-277.

DOI: 10.1016/j.jsv.2005.11.002

[3] L. Wang, L. Jiao and G. Shi, et al.: Wigner-Ville distribution based on EMD for faults diagnosis of bearing, Springer LNCS, Vol. 4223 (2006), pp.803-812.

[4] Z. Peng, P. Tse and F. Chu: A comparison study of improved Hilbert-Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing, Mechanical Systems and Signal Processing, Vol. 19 (2005), pp.974-988.

DOI: 10.1016/j.ymssp.2004.01.006

[5] N. Tandon and A. Choudhury: A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings, Tribology International, Vol. 32 (1999), pp.469-480.

DOI: 10.1016/s0301-679x(99)00077-8

[6] X. Lou and K. Loparo: Bearing fault diagnosis based on wavelet transform and fuzzy inference, Mechanical Systems and Signal Processing, Vol. 18 (2004), pp.1077-1095.

DOI: 10.1016/s0888-3270(03)00077-3

[7] S. Prabhakar, A. Mohanty and A. Sekhar Application of discrete wavelet transform for detection of ball bearing race faults, Tribology International, Vol. 35 (2002) 793-800.

DOI: 10.1016/s0301-679x(02)00063-4

[8] A. Djebala, N. Ouelaa, and N. Hamzaoui: Detection of rolling bearing defects using discrete wavelet analysis, Meccanica, Vol. 43 (2008), pp.339-348.

DOI: 10.1007/s11012-007-9098-y

[9] J. E. Jackson: A User's Guide to Principal Components, John Wiley & Sons Inc, New York, (1991).

[10] A. Lachouri, K. Baiche, R. Djeghader, et al.: Analyze and fault diagnosis by multi-scale PCA, in: Proceedings of the 3rd International Conference on Information and Communication Technologies, Vol. 1-5 (2008), Damascus, SYRIA, pp.901-906.

DOI: 10.1109/ictta.2008.4530069

[11] S. Lu and M. Li: Bearing fault diagnosis based on PCA and SVM, in: Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, Vol. 1-5 (2007), Harbin, CHINA, pp.3503-3507.

DOI: 10.1109/icma.2007.4304127

[12] A. Malhi and R. Gao: PCA-based feature selection scheme for machine defect classification, IEEE Transactions on Instrumentation and Measurement, Vol. 53 (2004), pp.1517-1525.

DOI: 10.1109/tim.2004.834070

[13] J. Lin and M. Zuo: Gearbox fault diagnosis using adaptive wavelet filter, Mechanical Systems and Signal Processing, vol. 17 (2003), pp.1259-1269.

DOI: 10.1006/mssp.2002.1507

[14] J. M. Keller, M. R. Gray and J. A. Givens: A Fuzzy K-Nearest Neighbor Algorithm, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 15 (1985), pp.580-585.

DOI: 10.1109/tsmc.1985.6313426

[15] J. Sim, S. Kim and J. Lee: Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method, Bioinformatics, Vol. 21 (2005), pp.2844-2849.

DOI: 10.1093/bioinformatics/bti423