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Anomaly Detection of Wind Turbine Driveline Based on Sequence Decomposition Interactive Network

Aimed at identifying the health state of wind turbines (WTs) accurately by using the comprehensive spatio and temporal information from the supervisory control and data acquisition (SCADA) data, a novel anomaly-detection method called decomposed sequence interactive network (DSI-Net) is proposed in...

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Detalles Bibliográficos
Autores principales: Lyu, Qiucheng, He, Yuwei, Wu, Shijing, Li, Deng, Wang, Xiaosun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648590/
https://www.ncbi.nlm.nih.gov/pubmed/37960662
http://dx.doi.org/10.3390/s23218964
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author Lyu, Qiucheng
He, Yuwei
Wu, Shijing
Li, Deng
Wang, Xiaosun
author_facet Lyu, Qiucheng
He, Yuwei
Wu, Shijing
Li, Deng
Wang, Xiaosun
author_sort Lyu, Qiucheng
collection PubMed
description Aimed at identifying the health state of wind turbines (WTs) accurately by using the comprehensive spatio and temporal information from the supervisory control and data acquisition (SCADA) data, a novel anomaly-detection method called decomposed sequence interactive network (DSI-Net) is proposed in this paper. Firstly, a DSI-Net model is trained using preprocessed data from a healthy state. Subsequences of trend and seasonality are obtained by DSI-Net, which can dig out underlying features both in spatio and temporal dimensions through the interactive learning process. Subsequently, the trained model processes the online data and calculates the residual between true values and predicted values. To identify anomalies of the WTs, the residual and root mean square error (RMSE) are calculated and processed by exponential weighted moving average (EWMA). The proposed method is validated to be more effective than the existing models according to the control experiments.
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spelling pubmed-106485902023-11-03 Anomaly Detection of Wind Turbine Driveline Based on Sequence Decomposition Interactive Network Lyu, Qiucheng He, Yuwei Wu, Shijing Li, Deng Wang, Xiaosun Sensors (Basel) Article Aimed at identifying the health state of wind turbines (WTs) accurately by using the comprehensive spatio and temporal information from the supervisory control and data acquisition (SCADA) data, a novel anomaly-detection method called decomposed sequence interactive network (DSI-Net) is proposed in this paper. Firstly, a DSI-Net model is trained using preprocessed data from a healthy state. Subsequences of trend and seasonality are obtained by DSI-Net, which can dig out underlying features both in spatio and temporal dimensions through the interactive learning process. Subsequently, the trained model processes the online data and calculates the residual between true values and predicted values. To identify anomalies of the WTs, the residual and root mean square error (RMSE) are calculated and processed by exponential weighted moving average (EWMA). The proposed method is validated to be more effective than the existing models according to the control experiments. MDPI 2023-11-03 /pmc/articles/PMC10648590/ /pubmed/37960662 http://dx.doi.org/10.3390/s23218964 Text en © 2023 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
Lyu, Qiucheng
He, Yuwei
Wu, Shijing
Li, Deng
Wang, Xiaosun
Anomaly Detection of Wind Turbine Driveline Based on Sequence Decomposition Interactive Network
title Anomaly Detection of Wind Turbine Driveline Based on Sequence Decomposition Interactive Network
title_full Anomaly Detection of Wind Turbine Driveline Based on Sequence Decomposition Interactive Network
title_fullStr Anomaly Detection of Wind Turbine Driveline Based on Sequence Decomposition Interactive Network
title_full_unstemmed Anomaly Detection of Wind Turbine Driveline Based on Sequence Decomposition Interactive Network
title_short Anomaly Detection of Wind Turbine Driveline Based on Sequence Decomposition Interactive Network
title_sort anomaly detection of wind turbine driveline based on sequence decomposition interactive network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648590/
https://www.ncbi.nlm.nih.gov/pubmed/37960662
http://dx.doi.org/10.3390/s23218964
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