title: Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal.
abstract: There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.
authors.affiliation: Control Systems Department, Universidad de Los Andes, Mérida 5101, Venezuela. cerradam@ula.ve.|Mechanical Engineering Department, Universidad Politécnica Salesiana, Cuenca 010150, Ecuador. cerradam@ula.ve.
authors.forename: Mariela
authors.initials: M
authors.lastname: Cerrada
authors.affiliation: Mechanical Engineering Department, Universidad Politécnica Salesiana, Cuenca 010150, Ecuador. rsanchezl@ups.edu.ec.|Mechanics Department, Universidad Nacional de Educación a Distancia, Madrid 28040, Spain. rsanchezl@ups.edu.ec.
authors.forename: René
authors.initials: R
authors.lastname: Vinicio Sánchez
authors.affiliation: Mechanical Engineering Department, Universidad Politécnica Salesiana, Cuenca 010150, Ecuador. dcabrera@ups.edu.ec.
authors.forename: Diego
authors.initials: D
authors.lastname: Cabrera
authors.affiliation: Mechanical Engineering Department, Universidad Politécnica Salesiana, Cuenca 010150, Ecuador. gzuritav@ups.edu.ec.
authors.forename: Grover
authors.initials: G
authors.lastname: Zurita
authors.affiliation: Chongqing Key Laboratory of Manufacturing Equipment Mechanism Design and Control, Chongqing Technology and Business University, Chongqing 400067, China. chuanli@21cn.com.
authors.forename: Chuan
authors.initials: C
authors.lastname: Li
keywords: fault diagnosis; feature selection; gearbox; genetic algorithms; neural networks; vibration signal