Introduce the Quantitative Identification Method of Rolling Bearing in the Application of Fault Detection

Abstract:

Rolling bearing is an important part of rotating machinery. Its failure will directly affect the normal operation of the whole machinery. This study proposed an intelligent diagnosis model based on Fuzzy support vector description for the quantitative identification of bearing fault. The proposed model constructs the spherically shaped decision boundary by training the features of normal bearing data, and then calculates the fuzzy monitoring coefficient to identify the bearing damage.

• Periodical: Applied Mechanics and Materials (Volume 742)
• Pages: 147-149
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.742.147
• Online since: March 2015
• Authors: Li Huo*
• Keywords: Fault Detection, Quantitative Identification Method, Rolling Bearing

References

[1] http: /baike. so. com/doc/5328210. html.

[2] http: /www. docin. com/p-120926676. html.

[3] QiaoHu , ZhengjiaHe , YanyangZi , Intelligent monitoring and diagnosis of early fault based on fuzzy support vector data description[J] , Journal of Mechanical Engineering，2005, 41(12): 145~149.

DOI: 10.3901/jme.2005.12.145

[4] Tax D M J, Duin R P W, Support Vector data description[J]. Machine Learning, 2004, 54: 45~66.

DOI: 10.1023/b:mach.0000008084.60811.49