Fault Diagnosis Method of Mini Excavator Slewing Bearing

Abstract:

In order to collect the fault signal of slewing bearing, design and built up the slewing bearing test rig and the signal test system. Because slewing bearing fault signal is weak, the signal containing fault characteristics was resolved and reconstructed with the wavelet theory. With the application of the Hilbert transform in demodulation and detailed spectrum analysis, the fault characteristic frequency was extracted, and the slewing bearing fault was judged. All above work shows that Wavelet analysis combined with the Hilbert analysis is effective to the diagnosis of rotary bearing local fault.

• Periodical: Applied Mechanics and Materials (Volumes 541-542)
• Pages: 544-548
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.541-542.544
• Online since: March 2014
• Authors: Gang Tao Guo, Zhi Shan Duan, Li Chen Shi, Li Zhao
• Keywords: Fault Diagnosis, Hilbert Transform, Slewing Bearing, Wavelet

References

[1] Yu Xing, Yin Hong-sheng etc. Research on Fault Diagnosis of Coal Mine Main Ventilator Based on Principal Component and Wavelet Analysis [J]. Coal Mine Machinery, 2010 (4) : 238-240.

[2] Ye Yi-xi. Study large slewing device fault mechanisms and fault diagnosis technology [D]. ​​Wuhan University of Technology, 2005 (6) : 9-13.

[3] Zhou Wei. senior technical MATLAB wavelet analysis [M], Xi'an: Xi'an University of Electronic Science and Technology Press, (2005).

[4] Chen Yong-hui, Jiang Xu etc. Fault diagnosis of rolling bearings based on wavelet analysis and Hilbert transform [J]. Journal of Machine Design, 2010 (8) : 91-95.

[5] Bu Ling-li, GUO Jian-ying, Jiang Feng-lin. Application of Wavelet Analysis and Hilbert Ana lysis to Rolling Bearing Fault Diagnosis [J]. Harbin Institute of Technology, 2008 (2): 82-85.