Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost

•An anomaly detection and diagnosis method for wind turbines.•Abnormal data recognition algorithm based on LOF and adaptive K-means.•Normal behavior model based on LSTM-SDAE.•Anomaly location by contribution analysis and XGBoost. An anomaly detection and diagnosis method for wind turbines using long...

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Vydané v:	Reliability engineering & system safety Ročník 222; s. 108445
Hlavní autori:	Zhang, Chen, Hu, Di, Yang, Tao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Barking Elsevier Ltd 01.06.2022 Elsevier BV
Predmet:	Abnormalities Algorithms Anomalies Anomaly detection and diagnosis Confidence intervals Diagnosis Feature extraction Long short-term memory Long short-term memory (LSTM) Mahalanobis distance (MD) Noise reduction Outliers (statistics) Reliability engineering Stacked denoising autoencoders (SDAE) Statistical analysis Turbines Wind farms Wind power Wind turbine Wind turbines XGBoost Stacked denoising autoencoders (SDAE) Mahalanobis distance (MD) Anomaly detection and diagnosis Wind turbine Long short-term memory (LSTM) XGBoost
ISSN:	0951-8320, 1879-0836
On-line prístup:	Získať plný text
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