## 5 Conclusions  
In the present work, a multi-scale approach for fault detection in wind turbine gearboxes was developed and tested on a real-world test case. The novelty of the work is due to the fact that the detection approaches are developed using the industrial data sets available from the standard SCADA and TCM systems. This is the main challenge faced in the present paper and the results unveil the pros and cons in using each kind of datum. The early fault detection is possible using intuitive SCADA processing like the analysis of the \(\Delta P\) of the oil pressure before and after the gearbox filter. Even if this approach seems the most useful in the early fault diagnosis as the anomaly of the faulty machine has been clearly visible since the beginning (9 months before the turbine's stop, see Figure 1), this kind of analysis is not useful to identify the faulty component. A deeper diagnosis can be performed using the temperature measurements of the gearbox component recorded by the SCADA systems on a 10 min time scale. The raw trend of temperature is indeed not useful but the use of more complex modelling methods (like the SVR employed in this work) can unveil the abnormal positive drift of the error of model. The application of such an approach begins by establishing a baseline model using the dataset at the beginning of the period under investigation. When a fault in the gear is coming and producing heat, the actual temperature of the component is frequently higher than the temperature modelled by the SVR. In this way, temperature monitoring can be conducted, basing on the results of present work, a very powerful tool for early fault diagnosis as it allowed the discovery of the fault 5-6 months before the turbine's stop (see Figure 7b). Such a conclusion is different from what can be obtained using only raw temperature data that are usually considered only for late detection. The main limit of this approach is that a training period is always necessary so that, in our case, the first 3 months of data cannot be used to detect any anomaly. A joint analysis of \(\delta P\) (Figure 1) for the oil filter and temperature error (Figure 7) can only be synchronized using a larger dataset for temperature processing in order to create a trained reference model in the machine learning approach. The approach used for TCM vibration data analysis confirms that the use of high resolution measurements is one of the best options for early detection and can give quite detailed information about the real location of the fault. Anyway a critical issue in the interpretation of such results is that the precise location of the faulty component was not really discovered through the use of vibrations or SVR-temperature analysis. Both approaches clearly indicate a problem in the gear but the actual faulty component was the main bearing sustaining the main shaft at the interface with the gearbox. This can be discussed as follows:  
* The individuation of the damage through temperature analysis is easier as the overheating increases and this can be very likely produced near the high speed components of the gear also if the damage is not exactly located there;
* The detection of faults through vibration signals in the low speed components (such the main shaft) can be successful only when using sufficiently long time histories. We can finally conclude that the tested approaches are really powerful to use in fault diagnosis but the drawback of using measurements coming from the industrial SCADA and TCM systems prevented from precisely locating the affected component. Both approaches were able to indirectly detect the problem, because the misalignment of the main shaft was finally affecting also the operation of the gear. Conceptualization, F.C., A.P.D., F.N. and L.G. ; data curation, F.C., A.P.D. and F.N. ; formal analysis, F.C., A.P.D. and L.G. ; investigation, F.C., A.P.D., F.N. and L.G. ; methodology, F.C. ; A.P.D. and L.G. ; project administration, F.C. and L.G. ; software, A.P.D. and F.N. ; supervision, F.C.