Common challenges in fault diagnosis include the lack of labeled data and the need to build models for each domain, resulting in many models that require supervision. Transfer learning can help tackle these challenges by learning cross-domain knowledge. Many approaches still require at least some labeled data in the target domain, and often provide unexplainable results. To this end, we propose a supervised transfer learning framework for fault diagnosis in wind turbines that operates in an Anomaly-Space. This space was created using SCADA data and vibration data and was built and provided to us by our research partner. Data within the Anomaly-Space can be interpreted as anomaly scores for each component in the wind turbine, making each value intuitive to understand. We conducted cross-domain evaluation on the train set using popular supervised classifiers like Random Forest, Light-Gradient-Boosting-Machines and Multilayer Perceptron as metamodels for the diagnosis of bearing and sensor faults. The Multilayer Perceptron achieved the highest classification performance. This model was then used for a final evaluation in our test set. The results show, that the proposed framework is able to detect cross-domain faults in the test set with a high degree of accuracy by using one single classifier, which is a significant asset to the diagnostic team.

翻译：故障诊断中的常见挑战包括标记数据的缺乏以及需要为每个领域构建模型，这导致许多模型需要监督。迁移学习通过学习跨领域知识有助于应对这些挑战。许多方法仍然需要在目标领域至少拥有部分标记数据，且往往提供难以解释的结果。为此，我们提出了一种在异常空间中运行的、面向风力涡轮机故障诊断的监督式迁移学习框架。该空间是利用SCADA数据和振动数据创建的，由我们的研究合作伙伴构建并提供。异常空间内的数据可解释为风力涡轮机中每个部件的异常分数，使得每个值都易于直观理解。我们在训练集上使用流行的监督分类器（如随机森林、Light-Gradient-Boosting-Machines和多层感知器）作为元模型，对轴承和传感器故障进行了跨领域评估。多层感知器取得了最高的分类性能。随后，该模型被用于测试集的最终评估。结果表明，所提出的框架能够通过单一分类器以高精度检测测试集中的跨领域故障，这对诊断团队而言是一项显著优势。