用异常空间实现跨域故障诊断，仅靠一个模型就能高精度识别风电机组故障。
Supervised Transfer Learning Framework for Fault Diagnosis in Wind Turbines
- 在异常空间中构建跨域特征，融合SCADA与振动数据
- 多层感知机在跨域测试集上表现最优，准确率显著提升
- 无需每域重新标注，适合工业现场快速部署
风力机组故障诊断常面临标注数据不足和需为每个领域单独建模的挑战。本文提出一种基于监督迁移学习的故障诊断框架，运行于由我们的研究伙伴提供的异常空间。该空间利用SCADA数据和振动数据构建，其中每个数值可直观解释为对应部件的异常评分。我们在训练集上使用随机森林、Light-Gradient-Boosting-Machines和多层感知机等主流分类器进行跨域评估，其中多层感知机表现最佳。该模型随后在测试集上进行最终评估，结果表明：所提框架仅用一个分类器即可在测试集上以高精度检测跨域故障，对诊断团队具有重要实用价值。
原文摘要 · Abstract (English)
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.
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