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This review aims to explore effective hybrid machine learning frameworks for anomaly detection and predictive maintenance in SCADA systems for wind turbines.

June 13, 2026

A Comprehensive Review of Hybrid Machine Learning Frameworks for SCADA-Based Anomaly Detection and Predictive Maintenance in Wind Turbines

Key Points

This review aims to explore effective hybrid machine learning frameworks for anomaly detection and predictive maintenance in SCADA systems for wind turbines.
Survey of SCADA-based anomaly detection techniques and predictive maintenance strategies.
Analysis of data pre-processing pipelines, feature engineering methods, and machine learning algorithms.
Examination of hybrid deep learning models like LSTM, CNNs, and physics-informed neural networks.
Hybrid architectures show superior performance in feature extraction and anomaly sensitivity.
Challenges in model interpretability and computational efficiency are highlighted.
Emerging research areas include Explainable AI and federated learning for enhanced monitoring systems.

Abstract

The rapid proliferation of Supervisory Control and Data Acquisition (SCADA) systems in wind turbines has generated massive volumes of high-dimensional, multivariate time-series data, creating significant opportunities and challenges for intelligent anomaly detection in predictive maintenance. From a Computer Science and Engineering perspective, effective analysis of this data requires advanced machine learning and deep learning techniques capable of handling severe class imbalance, noise, concept drift, non-stationarity, and complex temporal dependencies. This review paper presents a comprehensive survey of SCADA-based anomaly detection techniques for wind turbine monitoring systems, with a strong emphasis on hybrid machine learning frameworks. We systematically examine data pre-processing pipelines, feature engineering methods, classical machine learning algorithms, deep learning architectures (such as LSTM, autoencoders, and CNNs), and state-of-the-art hybrid models that integrate representation learning, temporal modelling, ensemble methods, and contrastive learning. Special attention is given to hybrid architectures such as deep autoencoder-ensemble systems, CNN-LSTM with attention mechanisms, and physics-informed neural network hybrids, which have demonstrated superior performance in feature extraction, anomaly sensitivity, and generalization across varying operating conditions. Furthermore, this paper critically analyses key research challenges in industrial AI/ML, including computational efficiency, model interpretability, scalability to edge devices, and handling of imbalanced industrial datasets. Finally, emerging research directions such as Explainable AI (XAI), federated learning, physics-informed machine learning, and standardized benchmarking for SCADA anomaly detection are discussed to guide future work in intelligent industrial monitoring systems.

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