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\title{Physics-Informed Fault Classification and Anomaly Detection in Wind
Energy Systems using Deep CNN and Adaptive Elite PSO-XGBoost}



\author[1]{chunyao lee}%
\author[2]{Edu Daryl Maceren}%
\affil[1]{National Taiwan University of Science and Technology}%
\affil[2]{Chung Yuan Christian University}%


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  \date{April 25, 2026}


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\begin{abstract}
Wind energy systems require fault diagnosis that identifies faults
despite data inconsistencies. This study addresses challenges in
supervisory control and data acquisition (SCADA) systems for monitoring
wind turbine conditions from imbalanced data representation and error
vulnerability. It examines the efficacy of Adaptive Elite-Particle Swarm
Optimization (AEPSO)-tuned Extreme Gradient Boosting (XGBoost) on an
imbalanced SCADA dataset for wind turbine fault classification. The
methodology integrates the resampled SCADA dataset with t-distributed
Stochastic Neighbour Embedding represented deep learning features.
Employing AEPSO-XGBoost classifier trained on merged SCADA and deep
learning data from a physics-informed deep convolutional neural network
forms the basis of the fault classification model. The AEPSO-XGBoost
regressor is validated across three distinct rear bearing temperature
datasets, facilitating parameter optimization and model robustness.
Also, this study explores supervised and unsupervised anomaly detection
models using PDCNN and AEPSO-XGBoost with rear-bearing temperature data.
Findings exhibit substantial fault classification and prediction
enhancements by merging resampled SCADA data with deep learning
features. Moreover, results show that applying AEPSO-XGBoost can
significantly improve anomaly detection metrics. Through AEPSO-XGBoost's
efficacy in enhancing fault prediction within imbalanced SCADA datasets,
the study proposes an integrated framework for fault classification and
anomaly detection as an innovative predictive maintenance system for
wind energy systems.%
\end{abstract}%



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