Analog Circuit Fault Diagnosis based on Fuzzy Support Vector Machine and Kernel Density Estimation

摘要：

Because analog circuits such as abnormal noise contained in the information, to the support vector machine to build up the optimal classification brings difficulties, this paper proposes a new method for analog circuit fault diagnosis. First of all, time-domain signal extraction circuit statistical parameters, a set of fault characteristics and then use kernel density estimation method, proposed a form of fuzzy membership function construction, to eliminate the impact of noise characteristics. The establishment of such a membership functions with fuzzy support vector machines on the circuit fault diagnosis. Through the training of support vector machine fault diagnosis model was to achieve single-fault and multi-circuit fault diagnostic classification. The method is applied on CSTV filter circuit, the simulation experiment results show that the method can highlight the different characteristics of fault can be diagnosed correctly and effectively multi-fault types, comprehensive diagnostic accuracy of 95％, and the method for analog circuit fault diagnosis a new way. This technology has good prospects for engineering applications.

关键词： Analog Circuit Fault Diagnosis fuzzy support vector machine Kernel Density Function statistical characteristics

作者: Jing Tang Yun’an Hu Tao Lin Yu Chen

作者单位: Department of Control EngineeringNaval Aeronautics Engineering Institute AcademyYantai, China Department of Control Engineering Naval Aeronautics Engineering Institute Academy Yantai, China

会议类型: 国际会议

会议名称: 2010 3rd International Conference on Advanced Computer Theory and Engineering(2010年第三届先进计算机理论与工程国际会议 ICACTE 2010)

会议地点: 成都

会议语种:英文

页码: 1-5

在线出版日期: 2010-08-20（万方平台首次上网日期，不代表论文的发表时间）