Defects Diagnosis and Classification for Rolling Bearing Based on Mathematical Morphology

摘要：

The defects diagnosis and pattern classification are presented in this paper.Morphological pattern spectrum describes the shape characteristics of the inspected signal based on the morphological opening operation with multiscale structuring elements.The pattern spectrum entropy and the barycenter scale location of the spectrum curve are extracted as the feature vector presenting different defects of the rolling bearings.The support vector machinery (SVM) algorithm is adopted to distinguish different kinds of defective bearing signals. The recognition results of the SVM are ideal and more precise than that of the artificial neural network.The combination of the morphological pattern spectrum parameter analysis and the SVM algorithm is suitable for the on-line automated defect diagnosis of the rolling bearing.

关键词： mathematical morphology pattern spectrum entropy defect diagnosis classification

作者: Rujiang Hao Zhipeng Feng Fulei Chu

作者单位: Department of Mechanical Engineering Shijiazhuang Railway Institute Shijiazhuang,050043,Hebei Provin Institute of Vehicular Engineering University of Science and Technology Beijing Beijing,100083,China Department of Precision Instruments Tsinghua University Beijing 100084,China

会议类型: 国际会议

会议名称: 2009 8th International Conference on Reliability,Maintainability and Safety(第八届中国国际可靠性、维修性、安全性会议)

会议地点: 成都

会议语种:英文

页码: 817-821

在线出版日期: 2009-08-24（万方平台首次上网日期，不代表论文的发表时间）