## 4 Conclusions  
In this study, a simplistic artificial neural network implementation was used on condition indicator (CI) value monitoring results from three cases of wind turbine drivetrain bearing failures to improve detection sensitivity, without the need for labelled data for every type of conceivable failure case in the drivetrain before implementation. Feedforward neural networks with three hidden layers were set up for each bearing failure case. Rotational speed measurements of the gearbox output shaft, taken simultaneously as the vibration measurements, used as input to predict the constructed CI-values to trend the condition of the bearings experiencing defects on the inner raceways. In two cases, CI-values from two different methods of signal processing were used, where the enveloped vibration measurements were converted into the frequency domain by the fast Fourier transform (FFT) and the Wavelet Packet Transform (WPT). In the last failure case, enveloped FFT CI-values with differing measurement time and frequency range were used. By using CI-values and accompanying rotational speed values collected before the incipient defect developed to train individual networks for each set of CI-values, the relationship between the rotational speed and the CI-values is learnt and can be predicted for the whole dataset. This was then used to calculate the difference between the predicted values to the true CI-values, which will as the defect starts to affect the vibration measurements start to increase. The conclusions which could be drawn from the results are:  
* Monitoring the difference between the ANN's predicted and true CI-values was able to increase the relative sensitivity compared to monitoring the CI-values by themselves. In a gearbox output shaft bearing failure case, the normalized difference between the predictions and the true CI-values calculated from WPT spectra increases to a higher level compared to using the FFT spectra CI-values. By implementing the ANN into the analysis, the disadvantage of FFT spectra CI-values where the incipient increase is difficult to distinguish from the earlier variation in the data compared to the WPT spectra CI-values is eliminated. This as the normalized difference for both methods dramatically increases simultaneously.
* The variance in rotational speed of the gearbox output shaft during the measurement time of the vibration measurements was shown to detrimentally influence the results, with singular peaks appearing in the trended normalized difference value between the prediction and the CI-results. This increase in non-stationarity lowered the noise level of the spectrum and the predictions were thereby overestimated, causing peaks in the normalized difference interfering. Thereby, the elimination of CI-values where the rotational speed variance was deemed too high from the analysis is recommended.
* By implementing the proposed ANN procedure on the planet bearing failure case it was shown to improve the sensitivity to such a degree that the defect could be detected, which was not possible by the CI-values only.  
Conceptualization, D.S.; methodology, D.S.; software, D.S.; validation, D.S.; formal analysis, D.S.; investigation, D.S.; resources, D.S.; data curation, D.S.; writing--original draft preparation, D.S.; writing--review and editing, D.S., P.M. and K.B.; visualization, D.S.; supervision, P.M. and K.B.; project administration, P.M. and K.B.; funding acquisition, P.M. and K.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.