Fault Diagnosis Model based on Support Vector Machine and Genetic Algorithm

摘要：

Recently, the dominating difficulty that fault intelligent diagnosis system faces is terrible lack of typical fault samples, which badly prohibits the development of machinery fault intelligent diagnosis. Mainly according to the key problems of support vector machine need to resolve in fault intelligent diagnosis system, this paper makes more systemic and thorough researches in building fault classifiers, parameters optimization of kernel function. A decision directed acyclic graph fault diagnosis classification model based on parameters selected by genetic algorithm is proposed, abbreviated as GDDAG. Finally, GDDAG model is applied to rotor fault system, the testing results demonstrate that this model has very good classification precision and realizes the multi-faults diagnosis.

关键词： Support Vector Machine Genetic Algorithm Fault Diagnosis Rotating Machine

作者: Wei Niu Guoqing Wang Zhengjun Zhai Juan Cheng

作者单位: College of Computer, Northwestern Polytechnical University, Xian 710072, China Chinese Aeronautical Radio Electronics Research Institute, Shanghai 200233, China Xian Institute of Applied Optics, Xian 710065, China

会议类型: 国际会议

会议名称: 2011 3nd International Conference on Mechanical and Electronics Engineering(2011年第三届机械与电子工程国际会议 ICMEE2011)

会议地点: 合肥

会议语种:英文

页码: 2535-2539

在线出版日期: 2011-09-23（万方平台首次上网日期，不代表论文的发表时间）