摘要
在仅有轴承正常运行数据的小样本情况下,支持向量数据描述(SVDD)能通过对多维特征的融合实现滚动轴承的故障检测与状态评估,但特征向量空间分布的复杂程度会直接影响SVDD的效果。为此,提出了一种基于超球优化支持向量数据描述的滚动轴承故障检测方法,通过超球优化改善特征向量的空间分布以降低数据描述任务的难度,进而使得超球优化SVDD能更有效地识别出滚动轴承故障。多组试验表明:在不同转速、不同测点、不同类型的滚动轴承故障下,超球优化SVDD比传统的SVDD方法效果更优。
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.
作者
林桐
陈果
滕春禹
王云
欧阳文理
LIN Tong;CHEN Guo;TENG Chunyu;WANG Yun;OUYANG Wenli(College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;Avic China Aero-Polytechnology Establishment,Beijing 100028,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2019年第2期204-210,225,共8页
Journal of Vibration and Shock
关键词
支持向量数据描述(SVDD)
滚动轴承
超球优化
特征融合
故障检测
特征变换
support vector data description(SVDD)
rolling bearing
hypersphere optimization
feature fusion
fault detection
feature transformation
