融合多种自编码器，实现风电机组早期故障的无监督检测。
Hybrid Autoencoder-Based Framework for Early Fault Detection in Wind Turbines
- 用变分自编码器、LSTM和Transformer捕捉不同时间模式。
- 在真实数据上达到0.947的AUC-ROC，故障预警提前48小时。
- 无需标注数据，适合大规模风电运维场景。
风力发电机组的可靠性对可再生能源发展至关重要，早期故障检测能显著降低停机时间和维护成本。本文提出一种基于集成学习的深度学习框架，用于风电机组的无监督异常检测。该方法融合变分自编码器（VAE）、LSTM自编码器与Transformer架构，从高维SCADA数据中捕捉不同时间与上下文特征。通过独特特征工程流程提取时序、统计及频域指标，经由深度模型处理后，采用集成评分与自适应阈值判定异常，无需标签数据。在包含三个风电场共89年实际运行数据的CARE数据集上验证，该方法实现0.947的AUC-ROC，并可在故障发生前48小时完成预警。该方案有助于实现预测性维护，减少故障率，提升大规模风电系统运行效率，具有重要社会价值。
原文摘要 · Abstract (English)
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.
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