Anomalies in wind turbines pose significant risks of costly downtime and maintenance, underscoring the importance of early detection for reliable operation. However, conventional fault detection methods, often reliant on standalone anomaly detection models, struggle with generalization in such complex settings, leading to suboptimal prediction performance. To address this challenge, this proposes an ensemble technique pipeline to enhance robustness by combining multiple models for anomaly detection using condition monitoring system vibration data from selected wind turbine bearings. A fleet-based anomaly detection framework was applied and improved into a comprehensive ensemble pipeline. Thus, the novelty of this study lies in the in-depth evaluation of using ensemble techniques with anomaly detection models for condition monitoring system vibration data, providing insights into the effectiveness of such an approach. In the end, the proposed pipeline attained over 84% for the receiver operating characteristic curve (AUC) across components when deployed over real unseen data, achieving 98% for AUC for the main bearing through Majority-Ensemble, 89% for AUC for the gearbox high-speed shaft bearing under key nearest neighbor stacking, 84% for AUC for the generator drive-end bearing through Voting-Hard technique and 95% for AUC for the generator non-drive-end bearing under Voting-Soft. This study demonstrates ensembles can achieve robust anomaly detection for wind turbine components, addressing generalization challenges when backed by robust pipelines.

中文翻译：

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使用风力涡轮机传动系统组件振动数据进行基于车队的异常检测的集成学习框架。

风力涡轮机的异常会带来代价高昂的停机和维护的重大风险，这凸显了早期检测对于可靠运行的重要性。然而，传统的故障检测方法通常依赖于独立的异常检测模型，在如此复杂的设置中难以泛化，导致预测性能不佳。为了应对这一挑战，本文提出了一种集成技术管道，通过使用来自选定风力涡轮机轴承的状态监测系统振动数据组合多个模型进行异常检测，从而增强鲁棒性。基于车队的异常检测框架被应用并改进为综合的集成管道。因此，这项研究的新颖性在于深入评估使用集成技术和异常检测模型来获取状态监测系统振动数据，从而深入了解这种方法的有效性。最终，当部署在真实的未见数据上时，所提出的管道的各组件接收器操作特性曲线 (AUC) 达到了 84% 以上，通过 Majority-Ensemble 主轴承的 AUC 达到了 98%，齿轮箱的 AUC 达到了 89%关键最近邻堆叠下的高速轴轴承，通过 Voting-Hard 技术，发电机驱动端轴承的 AUC 为 84%，而 Voting-Soft 技术下，发电机非驱动端轴承的 AUC 为 95%。这项研究表明，集成可以实现风力涡轮机组件的强大异常检测，在强大的管道支持下解决泛化挑战。