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New Particle Filter Based on GA for Equipment Remaining Useful Life Prediction

Remaining useful life (RUL) prediction of equipment has important significance for guaranteeing production efficiency, reducing maintenance cost, and improving plant safety. This paper proposes a novel method based on an new particle filter (PF) for predicting equipment RUL. Genetic algorithm (GA) i...

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Detalles Bibliográficos
Autores principales: Li, Ke, Wu, Jingjing, Zhang, Qiuju, Su, Lei, Chen, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5421656/
https://www.ncbi.nlm.nih.gov/pubmed/28350341
http://dx.doi.org/10.3390/s17040696
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author Li, Ke
Wu, Jingjing
Zhang, Qiuju
Su, Lei
Chen, Peng
author_facet Li, Ke
Wu, Jingjing
Zhang, Qiuju
Su, Lei
Chen, Peng
author_sort Li, Ke
collection PubMed
description Remaining useful life (RUL) prediction of equipment has important significance for guaranteeing production efficiency, reducing maintenance cost, and improving plant safety. This paper proposes a novel method based on an new particle filter (PF) for predicting equipment RUL. Genetic algorithm (GA) is employed to improve the particle leanness problem that arises in traditional PF algorithms, and a time-varying auto regressive (TVAR) model and Akaike Information Criterion (AIC) are integrated to establish the dynamic model for PF. Moreover, starting prediction time (SPT) detection method based on hypothesis testing theory is presented, by which SPT of equipment RUL can be adaptively detected. In order to verify the effectiveness of the methods proposed in this study, a simulation test and the accelerating fatigue test of a rolling element bearing are designed for RUL prediction. The test results show the methods proposed in this study can accurately predict the RUL of the rolling element bearing, and it performs better than the traditional PF algorithm and support vector machine (SVM) in the RUL prediction.
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spelling pubmed-54216562017-05-12 New Particle Filter Based on GA for Equipment Remaining Useful Life Prediction Li, Ke Wu, Jingjing Zhang, Qiuju Su, Lei Chen, Peng Sensors (Basel) Article Remaining useful life (RUL) prediction of equipment has important significance for guaranteeing production efficiency, reducing maintenance cost, and improving plant safety. This paper proposes a novel method based on an new particle filter (PF) for predicting equipment RUL. Genetic algorithm (GA) is employed to improve the particle leanness problem that arises in traditional PF algorithms, and a time-varying auto regressive (TVAR) model and Akaike Information Criterion (AIC) are integrated to establish the dynamic model for PF. Moreover, starting prediction time (SPT) detection method based on hypothesis testing theory is presented, by which SPT of equipment RUL can be adaptively detected. In order to verify the effectiveness of the methods proposed in this study, a simulation test and the accelerating fatigue test of a rolling element bearing are designed for RUL prediction. The test results show the methods proposed in this study can accurately predict the RUL of the rolling element bearing, and it performs better than the traditional PF algorithm and support vector machine (SVM) in the RUL prediction. MDPI 2017-03-28 /pmc/articles/PMC5421656/ /pubmed/28350341 http://dx.doi.org/10.3390/s17040696 Text en © 2017 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
Li, Ke
Wu, Jingjing
Zhang, Qiuju
Su, Lei
Chen, Peng
New Particle Filter Based on GA for Equipment Remaining Useful Life Prediction
title New Particle Filter Based on GA for Equipment Remaining Useful Life Prediction
title_full New Particle Filter Based on GA for Equipment Remaining Useful Life Prediction
title_fullStr New Particle Filter Based on GA for Equipment Remaining Useful Life Prediction
title_full_unstemmed New Particle Filter Based on GA for Equipment Remaining Useful Life Prediction
title_short New Particle Filter Based on GA for Equipment Remaining Useful Life Prediction
title_sort new particle filter based on ga for equipment remaining useful life prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5421656/
https://www.ncbi.nlm.nih.gov/pubmed/28350341
http://dx.doi.org/10.3390/s17040696
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