Fault monitoring for axlebox bearing based on extenics and support vector data description
ZHAO Congcong1，ZHAO Yinghui2，BAI Yang3，LIU Yumei4
1.College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China;
2.Intelligent Connected Vehicle Development Institute, General R & D Institute of China FAW, Changchun 130011, China;
3.Technical Development Department, Chengdu Branch, Faw-Volkswagen Automotive Co., Ltd., Changchun 130011, China;
4.College of Traffic, Jilin University, Changchun 130022, China
Abstract：In order to monitor the fault state of axlebox bearings, a fault monitoring method based on extenics and support vector data description (SVDD) was proposed.This method made full use of the qualitative and quantitative description characteristic of extenics and the single value classification characteristic of SVDD.The operating state matter-element of an axlebox bearing was firstly constructed by feature extracting.And then, the single-valued classifier of SVDD was trained, and the classical domains of the feature parameters in the matter-element were obtained by finding the support vectors of the minimum hypersphere.Finally, the correlation function was used to evaluate the axlebox bearing's fault state qualitatively and quantitatively.The real vibration signal analysis of the axlebox bearing verifies the feasibility and effectiveness of the proposed method.
赵聪聪1，赵颖慧2，白杨3，刘玉梅4. 基于可拓学和SVDD的轴箱轴承故障监测[J]. 振动与冲击, 2020, 39(4): 63-68.
ZHAO Congcong1，ZHAO Yinghui2，BAI Yang3，LIU Yumei4. Fault monitoring for axlebox bearing based on extenics and support vector data description. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(4): 63-68.
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