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An Improved Feature Selection Method Based on Random Forest Algorithm for Wind Turbine Condition Monitoring
Feature selection and dimensionality reduction are important for the performance of wind turbine condition monitoring models using supervisory control and data acquisition (SCADA) data. In this paper, an improved random forest algorithm, namely Feature Simplification Random Forest (FS_RF), is propos...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8402606/ https://www.ncbi.nlm.nih.gov/pubmed/34451096 http://dx.doi.org/10.3390/s21165654 |
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author | Li, Guo Wang, Chensheng Zhang, Di Yang, Guang |
author_facet | Li, Guo Wang, Chensheng Zhang, Di Yang, Guang |
author_sort | Li, Guo |
collection | PubMed |
description | Feature selection and dimensionality reduction are important for the performance of wind turbine condition monitoring models using supervisory control and data acquisition (SCADA) data. In this paper, an improved random forest algorithm, namely Feature Simplification Random Forest (FS_RF), is proposed, which is capable of identifying features closely correlated with wind turbine working conditions. The Euclidian distances are employed to distinguish the weight of the same feature among different samples, and its importance is measured by means of the random forest algorithm. The selected features are finally verified by a two-layer gated recurrent unit (GRU) neural network facilitating condition monitoring. The experimental results demonstrate the capacity and effectiveness of the proposed method for wind turbine condition monitoring. |
format | Online Article Text |
id | pubmed-8402606 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84026062021-08-29 An Improved Feature Selection Method Based on Random Forest Algorithm for Wind Turbine Condition Monitoring Li, Guo Wang, Chensheng Zhang, Di Yang, Guang Sensors (Basel) Article Feature selection and dimensionality reduction are important for the performance of wind turbine condition monitoring models using supervisory control and data acquisition (SCADA) data. In this paper, an improved random forest algorithm, namely Feature Simplification Random Forest (FS_RF), is proposed, which is capable of identifying features closely correlated with wind turbine working conditions. The Euclidian distances are employed to distinguish the weight of the same feature among different samples, and its importance is measured by means of the random forest algorithm. The selected features are finally verified by a two-layer gated recurrent unit (GRU) neural network facilitating condition monitoring. The experimental results demonstrate the capacity and effectiveness of the proposed method for wind turbine condition monitoring. MDPI 2021-08-22 /pmc/articles/PMC8402606/ /pubmed/34451096 http://dx.doi.org/10.3390/s21165654 Text en © 2021 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 Li, Guo Wang, Chensheng Zhang, Di Yang, Guang An Improved Feature Selection Method Based on Random Forest Algorithm for Wind Turbine Condition Monitoring |
title | An Improved Feature Selection Method Based on Random Forest Algorithm for Wind Turbine Condition Monitoring |
title_full | An Improved Feature Selection Method Based on Random Forest Algorithm for Wind Turbine Condition Monitoring |
title_fullStr | An Improved Feature Selection Method Based on Random Forest Algorithm for Wind Turbine Condition Monitoring |
title_full_unstemmed | An Improved Feature Selection Method Based on Random Forest Algorithm for Wind Turbine Condition Monitoring |
title_short | An Improved Feature Selection Method Based on Random Forest Algorithm for Wind Turbine Condition Monitoring |
title_sort | improved feature selection method based on random forest algorithm for wind turbine condition monitoring |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8402606/ https://www.ncbi.nlm.nih.gov/pubmed/34451096 http://dx.doi.org/10.3390/s21165654 |
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