The Diagnosis of Tool Wear Based on RBF Neural Networks and D-S Evidence Theory

摘要：

In view of uncertain factors in the machining process, the paper puts forward a two-level information fusion method based on RBF neural network and D-S evidence theory. Three different signals were used to train and test three RBF neural networks and the outputs of three RBF networks were aggregated using the D-S evidence theory. Experiments show that the combination of RBF neural network and D-S evidence theory can improve the efficiency and accuracy of the tool wear fault diagnosis.

关键词： wear diagnosis RBF neural network D-S evidence theory

作者: Weiqing Cao Pan Fu Weilin Li

作者单位: School Of Mechanical Engineering Southwest Jiaotong University Sichuan province,China School Of Mechanical Engineering Southwest Jiaotong University Sichuan province, China

会议类型: 国际会议

会议名称: 2010 3rd IEEE International Conference on Computer Science and Information Technology(第三届IEEE计算机科学与信息技术国际会议 ICCSIT 2010)

会议地点: 成都

会议语种:英文

页码: 409-411

在线出版日期: 2010-07-07（万方平台首次上网日期，不代表论文的发表时间）