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 (naive 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.