Extracting meaningful statistical features from the signals processed in the time domain, it was possible to distinguish faulty wind turbines with respect to healthy ones. This study, similarly to [32], is a collaboration between the ENGIE Italia company, the University of Perugia and the Politecnico di Torino. The main idea of this work is to analyze only data coming from the industrial SCADA and TCM systems of wind turbines operating in field and the desirable objective is to formulate a methodology for periodically supervising wind farms, which can be integrated into industrial practice. The combined use of SCADA and TCM data, proposed in this study, is used for practical application on a critical issue which is addressed by many studies in the literature, especially those employing test rig data: the fact that one already knows what component is damaged, independently of the methodology which is formulated in the study. Of course, the scientific method compensates for this upside-down approach with respect to the real-world problem, but the issue remains as regards practical applications: if in wind farm practice one starts from knowing nothing about the health state of a set of wind turbines, it is more complicated to formulate robust methodologies. On these grounds, the methodology proposed in this study is based on two steps, which use different information sources on different time scales. In summary, it proceeds as follows:  
* Historical SCADA data are employed for analyzing the wind farm trend of oil filter pressure. This study provides the first consultation about the suspect damaged wind turbines, which are selected as the target in the following step, and identifies the healthy wind turbines, which can be selected as the target. SCADA analysis is then completed with the study of long term trends of temperature measured on the drivetrain components. * TCM vibration data from the selected reference and target wind turbines are analyzed in the time domain, using methods similar to those in [32], and it is questioned whether there is a statistical novelty of the latter with respect to the former. This kind of result constitutes a basis for damage diagnosis. It should be noticed that the co-integration of SCADA and TCM data for damage diagnosis is an innovative approach, because typically the former or the latter information source are used separately. A recent attempt has been formulated in [34], but the point of view is slightly different with respect to the present study. Actually, in [34], time-resolved SCADA data are employed with the objective of pushing SCADA data analysis to time scales similar to TCM vibration data. The industrial application of the methodology in [34] is complex and the approach of the present study is instead based on recognizing that the time scale of SCADA and TCM data, which are relatively easily accessible in practice, is intrinsically very different: therefore, in this study it was selected to use them in two steps for different purposes (respectively, first advice and final damage diagnosis). The test case selected for this study, in collaboration with the ENGIE Italia company, is a wind farm featuring nine Vestas V90 wind turbines, each with 2 MW of rated power: it was selected because of its practical meaningfulness since it is a complex site, characterized by frequent operation under load reduction mode. Therefore, summarizing, the structure of the manuscript is the following: in Section 2, the test case and the data sets' structure are described; Section 3 is devoted the description of the methods; results are collected in Section 4; conclusions are drawn and further directions are indicated in Section 5.