A Two Stage Feature Selection method for Gear Fault Diagnosis Using ReliefF and GA-Wrapper
This paper presents a novel two stage feature selection method for gear fault diagnosis based on ReliefF and genetic algorithm. Prior to the feature selection, 114 parameters were extracted as the original feature set based on EMD, AR model, statistical methods and entropy. Then the ReliefF was employed to evaluate the quality of every individual feature and a sequential feature sets were obtained according to the marks evaluated by ReliefF. Then the cross validation technique was used to get the candidate feature set from the sequential sets. At the second stage, the genetic algorithm was utilized to search a more compact feature set based on the candidate set. Three different classifiers means the LDC (linear discriminant classifier), KNNC (k nearest neighbors classifier) and NBC (na(i)ve bayes classifier) were employed to evaluate the proposed method The application results to the real gear fault diagnosis have shown that the proposed method can obtain a higher performance with a small size feature set.
ReliefF GA feature selection gear fault diagnosis
Bing Li Peilin Zhang Guoquan Ren Zhi Xing
Mechanical Engineering College,First Department,Shi Jiazhuang,China,050003
国际会议
长沙
英文
578-581
2009-04-11（万方平台首次上网日期，不代表论文的发表时间）