Timely damage diagnosis of wind turbine rolling elements is a keystone for improving availability and eventually diminishing the cost of wind energy: from this point of view, it is a priority to integrate high-level practices into the real-world operation and maintenance of wind farms. On this basis, the present study is devoted to the formulation of reliable methodologies for the supervision of wind turbine bearings, which possibly can be integrated in the industrial practice. For this reason, this study is a collaboration between a company (ENGIE Italia), the University of Perugia and the Politecnico di Torino. The analysis is based on the exploitation of the data types which are available to wind farm managers from industrial control systems: SCADA (Supervisory Control And Data Acquisition) and TCM (Turbine Condition Monitoring). Due to the intrinsic sampling time difference between SCADA and TCM data (a few minutes the former, up to the millisecond for the latter), the proposed methodology is designed as multi-scale. At first, historical SCADA data are processed and the behavior of the oil filter pressure is analyzed for all the wind turbines in the farm: this provides preliminary advice for identifying presumably healthy wind turbines from those suspected of damage. A second step for the SCADA analysis is then represented by the study of the temperature trends of the bearings through a Support Vector Regression: the incoming damage is individuated from the analysis of the mismatch between measurements and estimates provided by the normal behavior model. Finally, the healthy units are selected as the reference and the faulty as the target for the analysis of TCM vibration data in the time domain: statistical features are computed on independent chunks of the signals and, using a Novelty Index, it was possible to distinguish the damaged wind turbines with respect to the reference ones. In light of the interest in application of the proposed methodology, good practice criteria in selecting and managing the data are discussed as well.

中文翻译：

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使用工业 SCADA 和振动数据的多尺度风力涡轮机轴承监控技术

风力发电机滚动元件的及时损坏诊断是提高可用性并最终降低风能成本的关键：从这个角度来看，将高水平实践融入风电场的实际运行和维护中是当务之急. 在此基础上，本研究致力于制定可靠的风力涡轮机轴承监测方法，这些方法可能可以整合到工业实践中。因此，这项研究是一家公司 (ENGIE Italia)、佩鲁贾大学和都灵理工大学之间的合作。该分析基于对工业控制系统中风电场管理者可用的数据类型的开发：SCADA（监督控制和数据采集）和 TCM（涡轮机状态监测）。由于 SCADA 和 TCM 数据之间的固有采样时间差异（前者几分钟，后者高达毫秒），所提出的方法被设计为多尺度。首先，处理历史 SCADA 数据并分析场中所有风力涡轮机的油过滤器压力行为：这为从怀疑损坏的风力涡轮机中识别可能健康的风力涡轮机提供初步建议。SCADA 分析的第二步是通过支持向量回归研究轴承的温度趋势：传入的损坏是从正常行为模型提供的测量值和估计值之间的不匹配分析中区分出来的。最后，选择健康单元作为参考，将故障单元作为时域中 TCM 振动数据分析的目标：在独立的信号块上计算统计特征，并使用新颖性指数区分损坏的单元风力涡轮机相对于参考涡轮机。鉴于对建议方法的应用感兴趣，还讨论了选择和管理数据的良好实践标准。