Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis

In this paper, a system based on artificial neural networks (ANNs) was designed to diagnose different types of fault in a gearbox. An experimental set of data was used to verify the effectiveness and accuracy of the proposed method. The system was optimized by eliminating unimportant features using a feature selection method (UTA method). Consequently, the fault detection system operates faster while the classification error decreases or remains constant in some other cases. This method of feature selection is compared with Genetic Algorithm (GA) results. The findings verify that the results of the UTA method are as accurate as GA, despite its simple algorithm.