Research graph
References from Early anomaly detection in wind Turbines by Causality-based graph attention networks. Local targets link to admitted publications; unresolved targets remain external evidence.
Unresolved reference
External reference
Unresolved reference
2024 · External reference
Performance and reliability of wind turbines: a review
2017 · External reference
10.3390/s21051686
10.3390/s21051686 · External reference
Fault diagnosis of industrial wind turbine blade bearing using acoustic emission analysis
10.1109/tim.2020.2969062 · 2020 · External reference
Fault detection of wind turbine gearbox using thermal network modelling and SCADA data
2020 · External reference
Cost-effective condition monitoring for wind turbines
10.1109/tie.2009.2032202 · 2010 · External reference
Unresolved reference
2025 · External reference
Anomaly-based fault detection in wind turbine main bearings
10.5194/wes-8-557-2023 · 2023 · External reference
Artificial intelligence in wind turbine fault detection and diagnosis: advances and perspectives
2025 · External reference
Application of artificial intelligence in wind power systems
2025 · External reference
10.1145/3439950
10.1145/3439950 · External reference
Data-driven models applied to predictive and prescriptive maintenance of wind turbine: a systematic review of approaches based on failure detection, diagnosis, and prognosis
2024 · External reference
10.1007/978-3-031-75010-6_28
10.1007/978-3-031-75010-6_28 · External reference
Feature selection for unsupervised defect detection of a wind turbine blade considering operational and environmental conditions
10.1016/j.ymssp.2025.112568 · 2025 · External reference
Autoencoder-based anomaly root cause analysis for wind turbines
10.1016/j.egyai.2021.100065 · 2021 · External reference
Transfer learning applications for autoencoder-based anomaly detection in wind turbines
10.1016/j.egyai.2024.100373 · 2024 · External reference
Unsupervised anomaly detection of permanent-magnet offshore wind generators through electrical and electromagnetic measurements
10.5194/wes-9-2063-2024 · 2024 · External reference
Fault detection in wind turbines using health index monitoring with variational autoencoders
10.5194/wes-10-2841-2025 · 2025 · External reference
Vibration anomaly detection of wind turbine based on temporal convolutional network and support vector data description
10.1016/j.engstruct.2024.117848 · 2024 · External reference
Anomaly detection on small wind turbine blades using deep learning algorithms
2024 · External reference
Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost
10.1016/j.ress.2022.108445 · 2022 · External reference
LSTM-autoencoder based anomaly detection using vibration data of wind turbines
10.3390/s24092833 · 2024 · External reference
Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning
10.1016/j.renene.2022.02.061 · 2022 · External reference
Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks
10.1016/j.apenergy.2021.117925 · 2022 · External reference
Abnormality detection method for wind turbine bearings based on CNN-LSTM
2023 · External reference
Fault detection in new wind turbines with limited data by generative transfer learning
10.1016/j.egyai.2025.100626 · 2025 · External reference
Anomaly detection for wind turbines using long short-term memory-based variational autoencoder wasserstein generation adversarial network under semi-supervised training
2023 · External reference
SGG-DGCN: wind turbine anomaly identification by using deep graph convolutional networks with similarity graph generation strategy
2024 · External reference
Labelling drifts in a fault detection system for wind turbine maintenance
2021 · External reference
Fault detection of control loops
2006 · External reference
The emerging graph neural networks for intelligent fault diagnostics and prognostics: a guideline and a benchmark study
10.1016/j.ymssp.2021.108653 · 2022 · External reference
Unresolved reference
2022 · External reference
A comprehensive survey on graph neural networks
10.1109/tnnls.2020.2978386 · 2020 · External reference
Deep anomaly detection in horizontal axis wind turbines using Graph Convolutional Autoencoders for Multivariate Time series
10.1016/j.egyai.2022.100145 · 2022 · External reference
10.3850/978-981-94-3281-3_esrel-sra-e2025-p9555-cd
10.3850/978-981-94-3281-3_esrel-sra-e2025-p9555-cd · External reference
Investigating causal relations by econometric models and cross-spectral methods
10.2307/1912791 · 1969 · External reference
Unresolved reference
External reference
Fault-tolerant control of wind turbines: a benchmark model
10.1109/tcst.2013.2259235 · 2013 · External reference
Unresolved reference
External reference
The graph neural network model
10.1109/tnn.2008.2005605 · 2009 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Unsupervised correlation- and interaction-aware anomaly detection for cyber-physical production systems based on graph neural networks
2024 · External reference
Improved fault detection and diagnosis using graph auto encoder and attention-based graph convolution networks
10.1016/j.dche.2024.100158 · 2024 · External reference
Advanced anomaly detection in smart grids using graph convolutional networks with integrated node and line sensor data
10.1109/access.2025.3595431 · 2025 · External reference
Wind turbine fault detection and identification via self-attention-based dynamic graph representation learning and variable-level normalizing flow
10.1016/j.ress.2024.110554 · 2025 · External reference
Connecting the dots: multivariate time series forecasting with graph neural networks
2020 · External reference
GRELEN: Multivariate time series anomaly detection from the perspective of graph relational learning
2022 · External reference
Unresolved reference
External reference
10.1007/978-3-031-78128-5_24
10.1007/978-3-031-78128-5_24 · External reference
Exploring spatio-temporal dynamics for enhanced wind turbine condition monitoring
10.1016/j.ymssp.2024.111841 · 2025 · External reference
Wind turbine anomaly detection and identification based on graph neural networks with decision interpretability
10.1088/1361-6501/ad6f33 · 2024 · External reference
Graph neural networks for virtual sensing in complex systems: addressing heterogeneous temporal dynamics
10.1016/j.ymssp.2025.112544 · 2025 · External reference
Graph spatio-temporal networks for condition monitoring of wind turbine
10.1109/tste.2024.3411884 · 2024 · External reference
Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning
10.1016/j.renene.2023.02.053 · 2023 · External reference
Unresolved reference
External reference
Comparing threshold selection methods for network anomaly detection
10.1109/access.2024.3452168 · 2024 · External reference
Unresolved reference
2020 · External reference
Distribution of the estimators for autoregressive time series with a unit root
10.2307/2286348 · 1979 · External reference
10.1109/tac.1974.1100705
10.1109/tac.1974.1100705 · External reference
Estimating the dimension of a model
10.1214/aos/1176344136 · 1978 · External reference
Measuring and relieving the over-smoothing problem for graph neural networks from the topological view
2020 · External reference
10.1109/iccd56317.2022.00048
10.1109/iccd56317.2022.00048 · External reference
First and second order Markov chain models for synthetic generation of wind speed time series
10.1016/j.energy.2004.05.026 · 2005 · External reference
Dropout: a simple way to prevent neural networks from overfitting
2014 · External reference
Unresolved reference
External reference
Leonardo: a pan-european pre-exascale supercomputer for HPC and AI applications
10.17815/jlsrf-8-186 · 2024 · External reference
Anomaly transformer: time series anomaly detection with association discrepancy
2022 · External reference
Unresolved reference
External reference
The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets
10.1371/journal.pone.0118432 · 2015 · External reference
Unresolved reference
External reference
10.1007/978-3-031-75010-6_28
10.1007/978-3-031-75010-6_28 · ExternalCitation · doi-reference
10.1007/978-3-031-78128-5_24
10.1007/978-3-031-78128-5_24 · ExternalCitation · doi-reference
Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks
10.1016/j.apenergy.2021.117925 · ExternalCitation · doi-reference
Improved fault detection and diagnosis using graph auto encoder and attention-based graph convolution networks
10.1016/j.dche.2024.100158 · ExternalCitation · doi-reference
Autoencoder-based anomaly root cause analysis for wind turbines
10.1016/j.egyai.2021.100065 · ExternalCitation · doi-reference
Deep anomaly detection in horizontal axis wind turbines using Graph Convolutional Autoencoders for Multivariate Time series
10.1016/j.egyai.2022.100145 · ExternalCitation · doi-reference
Transfer learning applications for autoencoder-based anomaly detection in wind turbines
10.1016/j.egyai.2024.100373 · ExternalCitation · doi-reference
Fault detection in new wind turbines with limited data by generative transfer learning
10.1016/j.egyai.2025.100626 · ExternalCitation · doi-reference
First and second order Markov chain models for synthetic generation of wind speed time series
10.1016/j.energy.2004.05.026 · ExternalCitation · doi-reference
Vibration anomaly detection of wind turbine based on temporal convolutional network and support vector data description
10.1016/j.engstruct.2024.117848 · ExternalCitation · doi-reference
Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning
10.1016/j.renene.2022.02.061 · ExternalCitation · doi-reference
Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning
10.1016/j.renene.2023.02.053 · ExternalCitation · doi-reference
Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost
10.1016/j.ress.2022.108445 · ExternalCitation · doi-reference
Wind turbine fault detection and identification via self-attention-based dynamic graph representation learning and variable-level normalizing flow
10.1016/j.ress.2024.110554 · ExternalCitation · doi-reference
The emerging graph neural networks for intelligent fault diagnostics and prognostics: a guideline and a benchmark study
10.1016/j.ymssp.2021.108653 · ExternalCitation · doi-reference
Exploring spatio-temporal dynamics for enhanced wind turbine condition monitoring
10.1016/j.ymssp.2024.111841 · ExternalCitation · doi-reference
Graph neural networks for virtual sensing in complex systems: addressing heterogeneous temporal dynamics
10.1016/j.ymssp.2025.112544 · ExternalCitation · doi-reference
Feature selection for unsupervised defect detection of a wind turbine blade considering operational and environmental conditions
10.1016/j.ymssp.2025.112568 · ExternalCitation · doi-reference
Wind turbine anomaly detection and identification based on graph neural networks with decision interpretability
10.1088/1361-6501/ad6f33 · ExternalCitation · doi-reference
Comparing threshold selection methods for network anomaly detection
10.1109/access.2024.3452168 · ExternalCitation · doi-reference
Advanced anomaly detection in smart grids using graph convolutional networks with integrated node and line sensor data
10.1109/access.2025.3595431 · ExternalCitation · doi-reference
10.1109/iccd56317.2022.00048
10.1109/iccd56317.2022.00048 · ExternalCitation · doi-reference
10.1109/tac.1974.1100705
10.1109/tac.1974.1100705 · ExternalCitation · doi-reference
Fault-tolerant control of wind turbines: a benchmark model
10.1109/tcst.2013.2259235 · ExternalCitation · doi-reference
Cost-effective condition monitoring for wind turbines
10.1109/tie.2009.2032202 · ExternalCitation · doi-reference
Fault diagnosis of industrial wind turbine blade bearing using acoustic emission analysis
10.1109/tim.2020.2969062 · ExternalCitation · doi-reference
The graph neural network model
10.1109/tnn.2008.2005605 · ExternalCitation · doi-reference
A comprehensive survey on graph neural networks
10.1109/tnnls.2020.2978386 · ExternalCitation · doi-reference
Graph spatio-temporal networks for condition monitoring of wind turbine
10.1109/tste.2024.3411884 · ExternalCitation · doi-reference
10.1145/3439950
10.1145/3439950 · ExternalCitation · doi-reference
Estimating the dimension of a model
10.1214/aos/1176344136 · ExternalCitation · doi-reference
The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets
10.1371/journal.pone.0118432 · ExternalCitation · doi-reference
Leonardo: a pan-european pre-exascale supercomputer for HPC and AI applications
10.17815/jlsrf-8-186 · ExternalCitation · doi-reference
Investigating causal relations by econometric models and cross-spectral methods
10.2307/1912791 · ExternalCitation · doi-reference
Distribution of the estimators for autoregressive time series with a unit root
10.2307/2286348 · ExternalCitation · doi-reference
10.3390/s21051686
10.3390/s21051686 · ExternalCitation · doi-reference
LSTM-autoencoder based anomaly detection using vibration data of wind turbines
10.3390/s24092833 · ExternalCitation · doi-reference
10.3850/978-981-94-3281-3_esrel-sra-e2025-p9555-cd
10.3850/978-981-94-3281-3_esrel-sra-e2025-p9555-cd · ExternalCitation · doi-reference
Fault detection in wind turbines using health index monitoring with variational autoencoders
10.5194/wes-10-2841-2025 · ExternalCitation · doi-reference
Anomaly-based fault detection in wind turbine main bearings
10.5194/wes-8-557-2023 · ExternalCitation · doi-reference
Unsupervised anomaly detection of permanent-magnet offshore wind generators through electrical and electromagnetic measurements
10.5194/wes-9-2063-2024 · ExternalCitation · doi-reference
