Research of Fault Diagnosis Based on Rough Sets and Support Vector Machine

摘要：

It is lack of fault samples and the feature information is miscellaneous and redundant in complex circuit system. In order to solve the problem, a new fault diagnosis method was presented based on rough set (RS) and support vector machine (SVM). The RS was applied to discrete sample data the genetic algorithm (GA) was used to reduce the redundant attributes and the conflicting samples. Then the simplest fault attributes were extracted as the training samples for SVM, which was used as the classifier to isolate the faults rapidly. The simulated experiments demonstrated that the method is valid and feasible under the condition of small samples.

关键词： fault diagnosis rough sets support vector machine genetic algorithm.

作者: Du Anli Wang Yingchun Wang Jie Hua Jiajun Shao Mengguo

作者单位: Missile Institute of Air Force Engineering University,Sanyuan 713800,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: 1210-1213

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）