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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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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. |
format | Online Article Text |
id | pubmed-5421656 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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