Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1785135373623492608 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10648590 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lyuqiucheng anomalydetectionofwindturbinedrivelinebasedonsequencedecompositioninteractivenetwork AT heyuwei anomalydetectionofwindturbinedrivelinebasedonsequencedecompositioninteractivenetwork AT wushijing anomalydetectionofwindturbinedrivelinebasedonsequencedecompositioninteractivenetwork AT lideng anomalydetectionofwindturbinedrivelinebasedonsequencedecompositioninteractivenetwork AT wangxiaosun anomalydetectionofwindturbinedrivelinebasedonsequencedecompositioninteractivenetwork |