# Detection of Wind Turbine Failures through Cross-Information between Neighbouring Turbines
## 5 Discussion  
Although the database has been exploited previously in other work to derive different normality models for the exploration of different subsystems, this technique uncovered subsets of data that were used in the training part of the model, but were mislabelled, i.e., used in the wrong class. While this is a relatively small proportion of the data, retraining the models with the more correctly labelled training data would provide a slight overall improvement in model performance. The strategy employed was to use the first half of the data for training, and although all machines had small stops in this time frame, some of these had not been detected. Thanks to the proposed method, if a turbine in the wind farm fails, the specific location of the failure can be determined as follows: Typically, WTs are analysed by subsystems. This means analysing the set of signals segregated into smaller groups according to the part of the WT they belong to. This allows faults to be isolated. Let us say, for example, that subsystem A is analysed and the failure occurs on subsystem B. What is observed in the signals belonging to subsystem A is that, if the failure is important enough to stop the WT, at the moments when the WT is stopped, peaks appear in the residuals and, obviously, if the WT is stopped, the rotor speed, the instantaneous power produced, etc., records are zero. In this sense, failure is not anticipated, but a way to automatically isolate the time windows in which the WT stops is obtained, which allows segmenting or correctly labelling sections of data to improve the training phase of the supervised models. In a context where wind farms have many WTs (more than a hundred, in many cases) and because each WT has hundreds of measurements recorded continuously for many years, applying this technique can be very helpful. It is even more interesting, however, when  
Figure 16: Analysis of the WT83 turbine on the basis of the residues obtained from the signals _wrtm_avg_Gbx_OilPress_ (upper signal, in black) and _wrtm_avg_TrmTmp_Gbx_ (lower signal, in black) together with the rotation of the rotor of the WT83 turbine (central part, in blue). The blue rectangles identify peaks in the residual signals that coincide with turbine stops, detected as zero-valued angular velocities. The red rectangle shows a ghost peak. Figure 17: Analysis of the WT84 turbine on the basis of the residues obtained from the signals _wrtm_avg_Gbx_OilPress_ (upper signal, in black) and _wrtm_avg_TrmTmp_Gbx_ (lower signal, in black) together with the rotation of the rotor of the WT84 turbine (central part, in blue). The blue rectangles identify peaks in the residual signals that coincide with turbine stops, detected as zero-valued angular velocities. the failure occurs in the signals of the system under analysis. In that case, if the failure is caused by the progressive deterioration of some mechanism, the signals involved begin to vary the pattern gradually and the failure can be anticipated. This occurs in Figure 17, corresponding to the analysis of the WT84. This failure affected the gearbox. The largest blue square selects the time interval where the WT failed. Here it is interesting to note that the peaks of the residuals, generated from signals from this subsystem, start to appear before the WT stops (zero-valued section in the central green signal). A brief analysis of the results found shows that they are consistent with what was observed in previous analyses of this area. The tests were carried out with the second part of the data of the turbines. As it is also reflected in the residue analysis, the first three turbines, WT80 (Figure 13), WT81 (Figure 14), and WT82 (Figure 15) have better health than the previous ones. The best of all is WT82 (Figure 15), which does not present any problem during the test period. The turbines WT81 and WT82 only have one problem that leaves them inactive for a short time.