Research on Analog Circuit Fault Feature Extraction Based on FRFT-KPCA Method

摘要：

In the fault diagnosis process of analog circuit, fault features extraction is an important technology. In order to gain effective features of nonstationary and time varying signals, the paper proposed an approach to extract fault features based on fractional Fourier transform (FRFT) and Kernel Principal Component Analysis (KPCA). Particle Swarm Optimization (PSO) is used to determine the optimal value of the fractional order p according to within-class and among-class scatter matrix. And mapping signals in an optimal FRFT domain for separation. Then, KPCA is used to compress the dimension of signal features. The experimental results show that after feature extraction by FRFT-KPCA approach, samples of different signals are well separated in fractional feature space.

关键词： fractional Fourier transform kernel principle component analysis feature extraction within-class and among-class scatter matrix

作者: Sun Jingjie Zhao Jianjun Sun Weimeng

作者单位: Graduate Students Brigade,Naval Aeronautical and Astronautical University ,Yantai 264001,China Department of Ordnance Science and Technology,Naval Aeronautical and Astronautical University,Yantai Ordnace revamping factory,Zhanjiang 524005,China

会议类型: 国际会议

会议名称: 2011 10th International Conference on Electronic Measurement & Instruments(第十届电子测量与仪器国际会议 ICEMI2011)

会议地点: 成都

会议语种:英文

页码: 1270-1274

在线出版日期: 2011-08-16（万方平台首次上网日期，不代表论文的发表时间）