STUDY OF MATHEMATICAL MORPHOLOGY AND SUPPORT VECTOR MACHINE ON FAULT CLASSIFICATION OF ROLLING BEARING

摘要：

A novel classification approach for the faulty rolling bearing is presented. Morphological pattern spectrum describes the shape characteristics of the signal. The pattern spectrum entropy and the barycenter scale location of the spectrum curve are selected as the feature vectors presenting different faults of the rolling bearings.The support vector machine(SVM) algorithm is adopted to distinguish six kinds of fault bearing signals. The recognition results of the SVM are ideal and more precise than that of the artificial neural network even though the training sample is few. The combination of the morphological pattern spectrum parameter analysis and the SVM algorithm is suitable for the on-line automated fault diagnosis of the rolling bearings. This application is promising and worth well exploiting.

关键词： Mathematical Morphology Support Vector Machine Faults Classification Rolling Bearing

作者: Rujiang Hao Wenxiu Lu Fulei Chu

作者单位: Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, China;Scho Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, China

会议类型: 国际会议

会议名称: the 3rd World Congress on Engineering Asset Management andIntelligent Maintenance Systems(第三届世界工程资产管理及智能维修学术大会)

会议地点: 北京

会议语种:英文

页码: 640-647

在线出版日期: 2008-10-27（万方平台首次上网日期，不代表论文的发表时间）