Multi-classifier Fusion Approach based on Data Clustering for Analog Circuits Fault Diagnosis

摘要：

When there are large amount of fault classes in analog circuits,normally single multi-class classifier cannot achieve satisfactory diagnosis accuracy because of its difficult training process. A method of multi-classifier fusion diagnosis approach based on data clustering is presented in this paper to improve fault diagnosis veracity. After extracting fault feature vectors by wavelet transform,fuzzy C-mean clustering algorithm is used to pre-partition the feature space into multiple sub-class groups as binary tree. According to the structure of the fault tree,multi-classifiers are created to form hierarchical diagnosis system. Simulation experiments demonstrate that the proposed approach for analog circuit fault diagnosis is superior to conventional ones. The fault diagnosis accuracy is greater than 98%.It has good performance in tackling large number of fault classes in analog circuits.

关键词： fault diagnosis analog circuits wavelet transform FCM clustering fusion

作者: Guoming Song Houjun Wang Hong Liu Shuyan Jiang

作者单位: School of Automation Engineering,University of Electronic Science and Technology of China,Chengdu,61 School of Automation Engineering,University of Electronic Science and Technology of China,Chengdu,61

会议类型: 国际会议

会议名称: 2009 IEEE 8th International Conference on ASIC(第八届IEEE国际专用集成电路大会)

会议地点: 长沙

会议语种:英文

页码: 1217-1220

在线出版日期: 2009-10-20（万方平台首次上网日期，不代表论文的发表时间）