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Failure Prediction and Replacement Strategies for Smart Electricity Meters Based on Field Failure Observation †

It is helpful to have a replacement strategy by predicting the number of failures of in-service electricity meters. This paper presents a failure number prediction method for smart electricity meters based on on-site fault data. The prediction model was constructed by combining Weibull distribution...

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Detalles Bibliográficos
Autores principales: Dong, Xianguang, Jing, Zhen, Dai, Yanjie, Wang, Pingxin, Chen, Zhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9788098/
https://www.ncbi.nlm.nih.gov/pubmed/36560173
http://dx.doi.org/10.3390/s22249804
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author Dong, Xianguang
Jing, Zhen
Dai, Yanjie
Wang, Pingxin
Chen, Zhen
author_facet Dong, Xianguang
Jing, Zhen
Dai, Yanjie
Wang, Pingxin
Chen, Zhen
author_sort Dong, Xianguang
collection PubMed
description It is helpful to have a replacement strategy by predicting the number of failures of in-service electricity meters. This paper presents a failure number prediction method for smart electricity meters based on on-site fault data. The prediction model was constructed by combining Weibull distribution with odds ratios, then the distribution parameters, failure prediction number, and confidence intervals of prediction number were calculated. A strategy of meter replacement and reserve were developed according to the prediction results. To avoid the uncertainty of prediction results due to the small amount of field data information, a Bayesian failure number prediction method was developed. The research results have value for making operation plans and reserve strategies for electricity meters.
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spelling pubmed-97880982022-12-24 Failure Prediction and Replacement Strategies for Smart Electricity Meters Based on Field Failure Observation † Dong, Xianguang Jing, Zhen Dai, Yanjie Wang, Pingxin Chen, Zhen Sensors (Basel) Article It is helpful to have a replacement strategy by predicting the number of failures of in-service electricity meters. This paper presents a failure number prediction method for smart electricity meters based on on-site fault data. The prediction model was constructed by combining Weibull distribution with odds ratios, then the distribution parameters, failure prediction number, and confidence intervals of prediction number were calculated. A strategy of meter replacement and reserve were developed according to the prediction results. To avoid the uncertainty of prediction results due to the small amount of field data information, a Bayesian failure number prediction method was developed. The research results have value for making operation plans and reserve strategies for electricity meters. MDPI 2022-12-14 /pmc/articles/PMC9788098/ /pubmed/36560173 http://dx.doi.org/10.3390/s22249804 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dong, Xianguang
Jing, Zhen
Dai, Yanjie
Wang, Pingxin
Chen, Zhen
Failure Prediction and Replacement Strategies for Smart Electricity Meters Based on Field Failure Observation †
title Failure Prediction and Replacement Strategies for Smart Electricity Meters Based on Field Failure Observation †
title_full Failure Prediction and Replacement Strategies for Smart Electricity Meters Based on Field Failure Observation †
title_fullStr Failure Prediction and Replacement Strategies for Smart Electricity Meters Based on Field Failure Observation †
title_full_unstemmed Failure Prediction and Replacement Strategies for Smart Electricity Meters Based on Field Failure Observation †
title_short Failure Prediction and Replacement Strategies for Smart Electricity Meters Based on Field Failure Observation †
title_sort failure prediction and replacement strategies for smart electricity meters based on field failure observation †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9788098/
https://www.ncbi.nlm.nih.gov/pubmed/36560173
http://dx.doi.org/10.3390/s22249804
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