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 Wang^1,2,*, Xi Kang¹, Cheng Wang¹, Tian-Bing Ma¹, Xiang He², Ke Yang^2,*

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 Authors: Sen-Hui Wang. Email: ; Ke Yang. Email:

Computer Modeling in Engineering & Sciences 2024, 140(2), 1405-1427. https://doi.org/10.32604/cmes.2024.049281

Received 02 January 2024; Accepted 25 March 2024; Issue published 20 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.

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Keywords

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Supplementary Material

Supplementary Material File

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