Wind Turbine Bearing Temperature Forecasting Using a New Data-Driven Ensemble Approach

The bearing temperature forecasting provide can provide early detection of the gearbox operating status of wind turbines. To achieve high precision and reliable performance in bearing temperature forecasting, a novel hybrid model is proposed in the paper, which is composed of three phases. Firstly,...

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Autores principales:	Guangxi Yan, Chengqing Yu, Yu Bai
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	bearing temperature forecasting hybrid model data decomposition optimization algorithm Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/153dfa27228540718c39409c0b0eb082
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id	oai:doaj.org-article:153dfa27228540718c39409c0b0eb082
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spelling	oai:doaj.org-article:153dfa27228540718c39409c0b0eb0822021-11-25T18:12:00ZWind Turbine Bearing Temperature Forecasting Using a New Data-Driven Ensemble Approach10.3390/machines91102482075-1702https://doaj.org/article/153dfa27228540718c39409c0b0eb0822021-10-01T00:00:00Zhttps://www.mdpi.com/2075-1702/9/11/248https://doaj.org/toc/2075-1702The bearing temperature forecasting provide can provide early detection of the gearbox operating status of wind turbines. To achieve high precision and reliable performance in bearing temperature forecasting, a novel hybrid model is proposed in the paper, which is composed of three phases. Firstly, the variational mode decomposition (VMD) method is employed to decompose raw bearing temperature data into several sub-series with different frequencies. Then, the SAE-GMDH method is utilized as the predictor in the subseries. The stacked autoencoder (SAE) is for the low-latitude features of raw data, while the group method of data handling (GMDH) is applied for the sub-series forecasting. Finally, the imperialist competitive algorithm (ICA) optimizes the weights for subseries and combines them to achieve the final forecasting results. By analytical investigation and comparing the final prediction results in all experiments, it can be summarized that (1) the proposed model has achieved excellent prediction outcome by integrating optimization algorithms with predictors; (2) the experiment results proved that the proposed model outperformed other selective models, with higher accuracies in all datasets, including three state-of-the-art models.Guangxi YanChengqing YuYu BaiMDPI AGarticlebearing temperature forecastinghybrid modeldata decompositionoptimization algorithmMechanical engineering and machineryTJ1-1570ENMachines, Vol 9, Iss 248, p 248 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bearing temperature forecasting hybrid model data decomposition optimization algorithm Mechanical engineering and machinery TJ1-1570
spellingShingle	bearing temperature forecasting hybrid model data decomposition optimization algorithm Mechanical engineering and machinery TJ1-1570 Guangxi Yan Chengqing Yu Yu Bai Wind Turbine Bearing Temperature Forecasting Using a New Data-Driven Ensemble Approach
description	The bearing temperature forecasting provide can provide early detection of the gearbox operating status of wind turbines. To achieve high precision and reliable performance in bearing temperature forecasting, a novel hybrid model is proposed in the paper, which is composed of three phases. Firstly, the variational mode decomposition (VMD) method is employed to decompose raw bearing temperature data into several sub-series with different frequencies. Then, the SAE-GMDH method is utilized as the predictor in the subseries. The stacked autoencoder (SAE) is for the low-latitude features of raw data, while the group method of data handling (GMDH) is applied for the sub-series forecasting. Finally, the imperialist competitive algorithm (ICA) optimizes the weights for subseries and combines them to achieve the final forecasting results. By analytical investigation and comparing the final prediction results in all experiments, it can be summarized that (1) the proposed model has achieved excellent prediction outcome by integrating optimization algorithms with predictors; (2) the experiment results proved that the proposed model outperformed other selective models, with higher accuracies in all datasets, including three state-of-the-art models.
format	article
author	Guangxi Yan Chengqing Yu Yu Bai
author_facet	Guangxi Yan Chengqing Yu Yu Bai
author_sort	Guangxi Yan
title	Wind Turbine Bearing Temperature Forecasting Using a New Data-Driven Ensemble Approach
title_short	Wind Turbine Bearing Temperature Forecasting Using a New Data-Driven Ensemble Approach
title_full	Wind Turbine Bearing Temperature Forecasting Using a New Data-Driven Ensemble Approach
title_fullStr	Wind Turbine Bearing Temperature Forecasting Using a New Data-Driven Ensemble Approach
title_full_unstemmed	Wind Turbine Bearing Temperature Forecasting Using a New Data-Driven Ensemble Approach
title_sort	wind turbine bearing temperature forecasting using a new data-driven ensemble approach
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/153dfa27228540718c39409c0b0eb082
work_keys_str_mv	AT guangxiyan windturbinebearingtemperatureforecastingusinganewdatadrivenensembleapproach AT chengqingyu windturbinebearingtemperatureforecastingusinganewdatadrivenensembleapproach AT yubai windturbinebearingtemperatureforecastingusinganewdatadrivenensembleapproach
_version_	1718411483202715648

Wind Turbine Bearing Temperature Forecasting Using a New Data-Driven Ensemble Approach

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