## 4 Analyses
### Results  
The resulting normalized information loss function values \(\Lambda(t)_{\Delta t_{\text{agg}}}\) were grouped into bins of error size. Then, the percentage of data in each bin was calculated, providing us an overview about the severity of the information loss for each signal after an aggregation in various resolutions. In Figure 5 the results are presented: each signal was assigned one bar lined up on the x-axis, colors represent the aforementioned error bins. The margins of these bins are defined as fractions of the normalization basis. The height of each bar is  
Figure 4: Exemplary illustration of the information loss \(\Lambda(t)_{600\,\text{s}}\) of the gearbox bearing temperature for a temporal aggregation of 600 s and a raw signal in 1 s resolution. given by the percentage of data in the corresponding error bin. The signals are sorted in ascending order from left to right on the horizontal axis according to the percentage of data in the lowest error bin of \([0,\,0.025]\times\mathrm{IQR}\) at the largest aggregation period of \(600\,\mathrm{s}\). Each signal is assigned a number in the bottom-most axes to allow for better orientation in the upper plots. This representation allowed us to yield an information loss added up from the local error, to compare different signals, and to determine the most affected signals. Moreover, it provided a quantification of the fraction of information that was lost for an assigned resolution for each signal. Tables with the numerical results are provided in the Supplementary Materials. Figure 5: Information loss results for all the available signals and various aggregation ranges. Error values are normalized and discretized into bins, defined as fractions of the IQR value. Colors represent the error fraction bins. Signals are identified by a number in the bottom-most row for improving the readability in the upper panes. When inspecting the results displayed in Figure 5, it is possible to obtain behaviors of the signals, such as critical drops of the information content passing from one resolution to another. Additionally, signals that are heavily affected by information loss can be identified. Some key observations are highlighted as follows:  
* The information loss severity, i.e., the maximum error occurring, varied greatly between the signals. Certain signals had roughly more than 1% of data with an error greater than 0.5 IQR, mainly environmental, electrical and control variables, such as frequency, currents, power factor, wind speed, and pitch angles. However, for the temperatures of gearbox shaft bearing 1 and the gearbox oil there was also a small amount of error above 0.5 IQR. * Generally, temperatures were not particularly affected. Only a fraction of information was lost for the largest aggregation period. A noticeable exception was the aforementioned transmission signals, i.e., gearbox shaft bearing 1 and 2 as well as the gearbox oil temperature, which had less than 50% of the data included in the lowest error bin at 600 s aggregation resolution and, therefore, underwent a relatively high loss of information. * Wind speed and electric signals underwent a drastic loss of information, even at the short aggregation periods (5 to 30 s). Wind speed data in particular featured only 40% of the data in the lowest error bin, i.e., an error \(\leq\)2.5% IQR. * Excluding the wind measurements, the pitch angles, electrical characteristics, and generator and rotor speed, information loss was limited to \(\approx\)18% of data with an error of \(>\)0.025% IQR up to an aggregation interval of 60 s. Above this threshold of 60 s resolution, most signals began to lose a considerable amount of information, as the shrinking percentage of data in the lowest error bins indicated.