Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

Grover Zurita,Chuan Li,René Sánchez,Diego Cabrera,Mariela Cerrada

doi:10.3390/s150923903

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 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%.

Highlights

Industrial environments have constantly increasing requirements for the continuous working of transmission machines
The analysis presented in Yan et al [38] provides an extensive overview of some of the latest efforts in the development and applications of Wavelet transform (WT) for fault diagnosis in rotating machinery
A multi-stage feature selection approach by using genetic algorithms (GA) has been proposed for designing fault diagnosis models

Summary

Introduction

Industrial environments have constantly increasing requirements for the continuous working of transmission machines. The availability of an important number of condition parameters that are extracted from rotating machinery signals, such as vibration signals, has motivated the use of machine learning-based fault diagnosis, where common approaches use neural networks (NN) and related models, because of the simplicity for developing industrial applications [9,10,11,12,13,14,15,16]. These approaches have been very useful for implementing condition-based maintenance (CBM), as is presented in Jardine et al [17]

Objectives

Methods

Results

Conclusion