research-article

An optimized support vector regression for prediction of bearing degradation

Authors:

Chenglong Zhang,

Zichen ZhangAuthors Info & Claims

Volume 113, Issue PB

https://doi.org/10.1016/j.asoc.2021.108008

Published: 01 December 2021 Publication History

Abstract

Machine learning and deep learning models are gradually being applied to predict the remaining useful life of bearings. But, still extracting effective features from the bearing signals and enhancing the prediction accuracy is a major concern. Therefore, an optimized support vector regression (SVR) model for prediction of bearing degradation is proposed. Firstly, the original time domain and frequency domain features extracted from the bearing vibration signal are learned by using the deep neural network (DNN) to improve the quality of degradation features. Secondly, a novel multi-population fruit fly optimization algorithm (MPFOA) is proposed by introducing multi-population mechanism. Thirdly, MPFOA is employed to choose the parameters of SVR, then we use MPFOA-SVR to predict the bearing remaining useful life through the degradation features learned by DNN. At last, CEC 2017 unconstrained benchmark functions and a real bearing dataset (IEEE 2012 PHM) are used to verify the performance of MPFOA and optimized SVR models respectively. Numerical experimental results show that MPFOA has a better optimization ability than the compared meta-heuristic algorithms. The optimized SVR model has a higher prediction accuracy in predicting the bearing remaining useful life.

Highlights

•

Multi-population fruit fly optimization algorithm is presented to optimize parameters of SVR.

•

Learning and increasing the sensitive of degradation features by DNN.

•

An optimized SVR model is applied to predict bearing degradation.

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Cited By

Sun YWang Z(2024)Remaining useful life prediction of rolling bearing via composite multiscale permutation entropy and Elman neural networkEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108852135:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108852
Zou LMa CHu JYu ZZhuang K(2024)Enhanced predictive modeling of rotating machinery remaining useful life by using separable convolution backbone networksApplied Soft Computing10.1016/j.asoc.2024.111493156:COnline publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111493
Hossein Rabiee ATahmasbi VQasemi M(2023)Experimental evaluation, modeling and sensitivity analysis of temperature and cutting force in bone micro-milling using support vector regression and EFAST methodsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.105874120:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.105874

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An optimized support vector regression for prediction of bearing degradation

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Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 113, Issue PB

Dec 2021

1240 pages

ISSN:1568-4946

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 December 2021

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Cited By

Sun YWang Z(2024)Remaining useful life prediction of rolling bearing via composite multiscale permutation entropy and Elman neural networkEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108852135:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108852
Zou LMa CHu JYu ZZhuang K(2024)Enhanced predictive modeling of rotating machinery remaining useful life by using separable convolution backbone networksApplied Soft Computing10.1016/j.asoc.2024.111493156:COnline publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111493
Hossein Rabiee ATahmasbi VQasemi M(2023)Experimental evaluation, modeling and sensitivity analysis of temperature and cutting force in bone micro-milling using support vector regression and EFAST methodsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.105874120:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.105874
Kumar LMurugan S(2023)Design of cuckoo search optimization with deep belief network for human activity recognition and classificationMultimedia Tools and Applications10.1007/s11042-023-14977-y82:19(29823-29841)Online publication date: 16-Mar-2023
https://dl.acm.org/doi/10.1007/s11042-023-14977-y

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