Submit

Login Login

Register Register

Home / Journals / CMES / Online First / doi:10.32604/cmes.2024.049281
Journal Menu
Special lssues
Table of Content

All issues

Online First

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

Open Access

ARTICLE

A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine

Sen-Hui Wang1,2,*, Xi Kang1, Cheng Wang1, Tian-Bing Ma1, Xiang He2, Ke Yang2,*
1 School of Mechatronics Engineering, Anhui University of Science and Technology, Huainan, 232001, China
2 Institute of Energy, Hefei Comprehensive National Science Center, Hefei, 230031, China
* Corresponding Author: Sen-Hui Wang. Email: email; Ke Yang. Email: email

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2024.049281

Received 02 January 2024; Accepted 25 March 2024; Published online 06 May 2024

Abstract

Accurately predicting the remaining useful life (RUL) of bearings in mining rotating equipment is vital for mining enterprises. This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features. This study proposes a hybrid predictive model to assess the RUL of rolling element bearings. The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features. The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm. Subsequently, the extreme learning machine (ELM) approach is applied to develop a predictive model of RUL based on the optimal features. The model is trained by optimizing its parameters via the grid search approach. The training datasets are adjusted to make them most suitable for the regression model using the cross-validation method. The proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing database. The comparison is constructed with other traditional models. The experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.

Keywords

Bearing degradation; remaining useful life estimation; RReliefF feature selection; extreme learning machine

  • 118

    View

  • 26

    Download

  • 0

    Like

Related articles
Copyright© 2024 Tech Science Press
© 1997-2024 TSP (Henderson, USA) unless otherwise stated

Share Link