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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-7512544 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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