Research on Application of Blind Source Separation in Rolling Bearing Fault Diagnosis Based on Particle Swarm Optimization

Article Preview

Abstract:

This paper presents blind source separation of rolling bearing based on particle swarm optimization. The algorithm combines the advantages of both blind source separation and particle swarm optimization. Through the experiment it is shown that the algorithm can separate the signals collected from rolling bearing and gearbox effectively, which can provide a new method for fault diagnosis and signal processing of machinery equipment.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 971-973)

Pages:

1321-1324

Citation:

Online since:

June 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] J Antoni, Journal of Sound and Vibration, 304(3–5): 497–529. (2007).

Google Scholar

[2] J Antoni, F Bonnardot, A Raad, M ElBadaoui, Mechanical System sand Signal Processing 18(6): 1285–1314. (2004).

Google Scholar

[3] Z K Zhu, Z H Feng, F R Kong, Mechanical Systems and Signal Processing. 19(3): 467–482. (2005).

Google Scholar

[4] Gelle G, Cloas M, Serviere C. IEEE. Tran. on Inst. and Meas. 2003, 52: 790~795.

Google Scholar

[5] Serviere C, Fabry P. Journal of Sound and Vibration, 2004, 272: 317~339.

Google Scholar

[6] R C Eberhart and J Kennedy, Proc. 6th Int. Symp. Micro Mach. Human, 1995, p.39–43.

Google Scholar

[7] Lazinlca A. Particle Swarm Optimization. I-Teeh Education and Publishing, (2007).

Google Scholar