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Hybrid Autoencoder-Based Framework for Early Fault Detection in Wind Turbines

Rekha R Nair, Tina Babu, Alavikunhu Panthakkan, Balamurugan Balusamy, Wathiq Mansoor

TL;DR

The study tackles early fault detection in wind turbines by framing anomaly detection as an unsupervised task implemented via a hybrid ensemble of Variational, LSTM, and Transformer Autoencoders. A automated feature-engineering pipeline extracts temporal, statistical, and frequency-domain indicators from high-dimensional SCADA data, enabling robust unsupervised learning without fault labels. The ensemble, with adaptive thresholding, achieves an AUC-ROC of $0.947$ on the CARE dataset and demonstrates fault detection up to $48$ hours before failure, highlighting practical potential for predictive maintenance in large wind-energy deployments. The approach offers interpretable insights through feature importance analyses and demonstrates cross-farm generalization, suggesting strong applicability to real-world wind turbine health monitoring and maintenance planning.

Abstract

Wind turbine reliability is critical to the growing renewable energy sector, where early fault detection significantly reduces downtime and maintenance costs. This paper introduces a novel ensemble-based deep learning framework for unsupervised anomaly detection in wind turbines. The method integrates Variational Autoencoders (VAE), LSTM Autoencoders, and Transformer architectures, each capturing different temporal and contextual patterns from high-dimensional SCADA data. A unique feature engineering pipeline extracts temporal, statistical, and frequency-domain indicators, which are then processed by the deep models. Ensemble scoring combines model predictions, followed by adaptive thresholding to detect operational anomalies without requiring labeled fault data. Evaluated on the CARE dataset containing 89 years of real-world turbine data across three wind farms, the proposed method achieves an AUC-ROC of 0.947 and early fault detection up to 48 hours prior to failure. This approach offers significant societal value by enabling predictive maintenance, reducing turbine failures, and enhancing operational efficiency in large-scale wind energy deployments.

Hybrid Autoencoder-Based Framework for Early Fault Detection in Wind Turbines

TL;DR

The study tackles early fault detection in wind turbines by framing anomaly detection as an unsupervised task implemented via a hybrid ensemble of Variational, LSTM, and Transformer Autoencoders. A automated feature-engineering pipeline extracts temporal, statistical, and frequency-domain indicators from high-dimensional SCADA data, enabling robust unsupervised learning without fault labels. The ensemble, with adaptive thresholding, achieves an AUC-ROC of on the CARE dataset and demonstrates fault detection up to hours before failure, highlighting practical potential for predictive maintenance in large wind-energy deployments. The approach offers interpretable insights through feature importance analyses and demonstrates cross-farm generalization, suggesting strong applicability to real-world wind turbine health monitoring and maintenance planning.

Abstract

Wind turbine reliability is critical to the growing renewable energy sector, where early fault detection significantly reduces downtime and maintenance costs. This paper introduces a novel ensemble-based deep learning framework for unsupervised anomaly detection in wind turbines. The method integrates Variational Autoencoders (VAE), LSTM Autoencoders, and Transformer architectures, each capturing different temporal and contextual patterns from high-dimensional SCADA data. A unique feature engineering pipeline extracts temporal, statistical, and frequency-domain indicators, which are then processed by the deep models. Ensemble scoring combines model predictions, followed by adaptive thresholding to detect operational anomalies without requiring labeled fault data. Evaluated on the CARE dataset containing 89 years of real-world turbine data across three wind farms, the proposed method achieves an AUC-ROC of 0.947 and early fault detection up to 48 hours prior to failure. This approach offers significant societal value by enabling predictive maintenance, reducing turbine failures, and enhancing operational efficiency in large-scale wind energy deployments.
Paper Structure (24 sections, 19 equations, 6 figures, 3 tables, 1 algorithm)

This paper contains 24 sections, 19 equations, 6 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Methodology
  3. Data Preprocessing and Feature Engineering
  4. Temporal Features
  5. Statistical Features
  6. Frequency Domain Features
  7. Normalization
  8. Deep Learning Models for Anomaly Detection
  9. Variational Autoencoder (VAE)
  10. LSTM Autoencoder
  11. Transformer Autoencoder
  12. Ensemble Scoring Mechanism
  13. Anomaly Thresholding
  14. Evaluation Metrics
  15. Threshold-Based Metrics
  16. ...and 9 more sections

Figures (6)

  • Figure 1: ROC Curves comparison for different models across three wind farms.
  • Figure 2: Precision-Recall curves for all models.
  • Figure 3: Distribution of anomaly scores for normal vs. anomalous sequences
  • Figure 4: Temporal anomaly detection example showing early fault detection
  • Figure 5: Top 20 most important features for anomaly detection across all wind farms
  • ...and 1 more figures