Rolling bearing fault detection based on the hypersphere optimization support vector data description

被引：0

作者：

Lin T. ^{[1
]}

Chen G. ^{[1
]}

Teng C. ^{[2
]}

Wang Y. ^{[2
]}

Ouyang W. ^{[2
]}

机构：

[1] College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing

[2] Avic China Aero-Polytechnology Establishment, Beijing

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2019年 / 38卷 / 02期

关键词：

Fault detection; Feature fusion; Feature transformation; Hypersphere optimization; Rolling bearing; Support vector data description(SVDD);

D O I：

10.13465/j.cnki.jvs.2019.02.030

中图分类号：

学科分类号：

摘要：

In the case of small sample size problems where only the operating data of healthy rolling bearings are available, the support vector data description (SVDD) method was applied to the rolling bearings fault detection and condition evaluation commendably by fusing multidimensional features. However, the complexity of the feature vector space distribution will directly affects the results of SVDD. Aiming at this, a novel rolling bearing fault detection method called hyper-sphere optimization support vector data description (hoSVDD) was proposed. The spatial distribution of feature vectors was improved by the hyper-sphere optimization so that the difficulty in data description was reduced. Hence, the hoSVDD is more suitable for rolling bearing fault detection. Multi-group rolling bearing tests show that: under the conditions of different speeds, different test points, and different types of rolling bearings faults, the proposed hoSVDD performs better than the traditional SVDD method. © 2019, Editorial Office of Journal of Vibration and Shock. All right reserved.

引用

页码：204 / 210and225

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