Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions

Bearing plays an important role in mechanical equipment, and its remaining useful life (RUL) prediction is an important research topic of mechanical equipment. To accurately predict the RUL of bearing, this paper proposes a data-driven RUL prediction method. First, the statistical method is used to...

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Autores principales: Zhang, Nannan, Wu, Lifeng, Wang, Zhonghua, Guan, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512544/
https://www.ncbi.nlm.nih.gov/pubmed/33266668
http://dx.doi.org/10.3390/e20120944
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author Zhang, Nannan
Wu, Lifeng
Wang, Zhonghua
Guan, Yong
author_facet Zhang, Nannan
Wu, Lifeng
Wang, Zhonghua
Guan, Yong
author_sort Zhang, Nannan
collection PubMed
description Bearing plays an important role in mechanical equipment, and its remaining useful life (RUL) prediction is an important research topic of mechanical equipment. To accurately predict the RUL of bearing, this paper proposes a data-driven RUL prediction method. First, the statistical method is used to extract the features of the signal, and the root mean square (RMS) is regarded as the main performance degradation index. Second, the correlation coefficient is used to select the statistical characteristics that have high correlation with the RMS. Then, In order to avoid the fluctuation of the statistical feature, the improved Weibull distributions (WD) algorithm is used to fit the fluctuation feature of bearing at different recession stages, which is used as input of Naive Bayes (NB) training stage. During the testing stage, the true fluctuation feature of the bearings are used as the input of NB. After the NB testing, five classes are obtained: health states and four states for bearing degradation. Finally, the exponential smoothing algorithm is used to smooth the five classes, and to predict the RUL of bearing. The experimental results show that the proposed method is effective for RUL prediction of bearing.
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spelling pubmed-75125442020-11-09 Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions Zhang, Nannan Wu, Lifeng Wang, Zhonghua Guan, Yong Entropy (Basel) Article Bearing plays an important role in mechanical equipment, and its remaining useful life (RUL) prediction is an important research topic of mechanical equipment. To accurately predict the RUL of bearing, this paper proposes a data-driven RUL prediction method. First, the statistical method is used to extract the features of the signal, and the root mean square (RMS) is regarded as the main performance degradation index. Second, the correlation coefficient is used to select the statistical characteristics that have high correlation with the RMS. Then, In order to avoid the fluctuation of the statistical feature, the improved Weibull distributions (WD) algorithm is used to fit the fluctuation feature of bearing at different recession stages, which is used as input of Naive Bayes (NB) training stage. During the testing stage, the true fluctuation feature of the bearings are used as the input of NB. After the NB testing, five classes are obtained: health states and four states for bearing degradation. Finally, the exponential smoothing algorithm is used to smooth the five classes, and to predict the RUL of bearing. The experimental results show that the proposed method is effective for RUL prediction of bearing. MDPI 2018-12-08 /pmc/articles/PMC7512544/ /pubmed/33266668 http://dx.doi.org/10.3390/e20120944 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Nannan
Wu, Lifeng
Wang, Zhonghua
Guan, Yong
Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions
title Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions
title_full Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions
title_fullStr Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions
title_full_unstemmed Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions
title_short Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions
title_sort bearing remaining useful life prediction based on naive bayes and weibull distributions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512544/
https://www.ncbi.nlm.nih.gov/pubmed/33266668
http://dx.doi.org/10.3390/e20120944
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