Analog Circuit Fault Diagnosis using AdaBoost with SVM-based Component Classifiers

摘要：

　　This paper presents a novel method of analog circuit fault diagnosis using AdaBoost with SVM-based component classifiers.We use binary-SVMs of o-a-r SVM as weak classifiers and design appropriate structure of SVM ensemble.Tent map is used to adjust parameters of SVM component classifiers for maintaining the diversity of weak classifiers.In simulation experiment,we use Monte-carlo analysis for 40kHz Sallen-Key bandpass filter and get transient response of thirteen faults.We extract feature vector by db3 wavelet packet transform and principal component analysis (PCA),and diagnose circuit faults by different methods.Simulation results show that the proposed method has the higher classification accuracy compared with other SVM methods.The generalization performance of ensemble method is good.It is suitable for practical use.

关键词： AdaBoost SVM analog circuit fault diagnosis

作者: Baoyu Dong Guang Ren

作者单位: Marine Engineering College,Dalian Maritime University,Dalian,116026;College of Electric and Informat Marine Engineering College,Dalian Maritime University,Dalian,116026

会议类型: 国际会议

会议名称: the 2012 International Conference on Manufacturing Engineering and Automation (2012年制造工程与自动化国际会议(ICMEA2012))

会议地点: 广州

会议语种:英文

页码: 1414-1417

在线出版日期: 2012-11-16（万方平台首次上网日期，不代表论文的发表时间）