## 3 Results and Discussion
### Failure Case 3 Results  
For failure case 3, CI-values directly extracted from the condition monitoring database were available which had been calculated from the FFT spectra of enveloped measurements with a 0-10 Hz frequency range. Concurrent CI-values from extracted measurement with a 0-5 kHz frequency range, which was thereafter enveloped and the FFT applied, were also used. The ANN implementation was employed for both these sets of CI-values to evaluate the impact of measurement properties on the monitoring results and if more detail could be obtained to yield an earlier detection of the defect. The ANN's predicted CI-values, the true enveloped FFT CI-values from measurements with a 0-5 kHz frequency range and the normalized difference between them can be seen in Figure 9.  
Figure 8: Failure case 2, planet bearing failure, enveloped WPT CI-values. Comparison of ANN’s predicted CI-values in red and true CI-values in blue at the top graph and normalized difference between them at the bottom graph.  
Figure 9: Gearbox output shaft bearing failure case 3, CI-values trended per day of the dataset starting at 0 from FFT spectra on enveloped measurements with a 0–5 kHz frequency range. Comparison of ANN’s predicted CI-values in red and true CI-values in blue at the top graph and normalized difference between them at the bottom graph.  
Note, the dataset in total spanned 731 days and was trended in accordance to this value, with the first defined as day 0, to be able to make a comparison to the 0-10 Hz frequency range CI-value dataset as the number of values in each dataset differ. For the first 120 days of the dataset, the rotational speed and thereby the predicted CI-values varied substantially. As these values constitute a large part of training the ANN algorithm, the ANN started to underperform slightly during long periods of high rotational speed. At day 728, there was a sudden increase of the true CI-values which the prediction could not replicate and thereby indicating the defect. When comparing these results to the previous CI-value results in the top graph of Figure 3, the indication of the defect was improved. As the sudden increase of the CI-values happened, the normalized difference between them and the ANN's prediction reached a level of 17.6, while the CI-value results only reached a level of 10.75. Therefore, the ANN implementation increased the sensitivity of the monitoring. However, it was not able to indicate the defect at an earlier stage.  
The ANN's predicted CI-values, the true enveloped FFT CI-values from the 0-10 kHz frequency range measurements and the normalized difference between them can be seen in Figure 10.  
The ANN could closely predict the CI-values for the first 400 days. Thereafter, the normalized difference between the predicted and true CI-values started periods of experiencing slight increases before decreasing again. At day 510, the normalized difference started to increase and fully differentiated from normal behaviour, clearly indicating the presence of the defect in the bearing. In the previous CI-value results in the bottom graph of Figure 3, this time period between day 400 and 510 with slight indications of the defect were not indistinguishable. Thereby, the implementation of the ANN had the potential to indicate the defect at a substantially earlier stage. Additionally, the information of the defect being present on the bearing raceway lay outside the measured properties of the stored measurements in the database and this disadvantage could not be solved with more intricate signal analysis and machine learning techniques.  
Figure 10: Gearbox output shaft bearing failure case 3, enveloped FFT CI-values from measurements with a 0–10 kHz frequency range trended per day of the dataset starting at 0. Comparison of ANN’s predicted CI-values in red and true CI-values in blue at the top graph and normalized difference between them at the bottom graph.