# On Training Data Selection in Condition Monitoring Applications--Case Azimuth Thrusters
## 4 Discussion  
The results provide guidance to automated training subset selection in condition monitoring applications. Firstly, the results in Section 3.5 indicate that the broadest operating areas for monitoring could be identified by using the KS and AD tests for the check of residuals' normal distribution. Secondly, the results in Sections 3.2 and 3.5 show that the novel approach to dataset segmentation, namely, the 'adaptive training subsets' approach, adapts well to different training datasets. With this approach, usable training subsets were identified irrespective of dataset size and distribution. The results further indicate that the previously introduced approach, named here as 'basic training subsets', has limited  
Figure 13: Speed ranges in selected training subsets based on system identification with five different hypothesis tests applied. Values on horizontal axis represent the sample size (N) and the vertical lines with different colors represent selected non-overlapping ranges. usability on large datasets, but it could be used with small datasets or distributions which have a shape close to a uniform distribution. The results show that the minimum width of a selected speed range should be constrained. The use of a narrow range is impractical for monitoring, unless the system is operated within the range regularly. The narrow range of the predictor variable, shaft rotational speed, results in wide confidence intervals for regression coefficients as shown in Figure 10. In such a case, a regression model may be useless unless it improves the normality of the samples used to define the multivariate normal distribution, which is the reference during monitoring, see [6]. In addition, the outlying samples from the edges of distribution should be omitted from the automated training subset selection, which was illustrated in Appendix C. A practical approach is to automatically select the operating areas for monitoring from a narrower interval, such as 1-99 th percentiles, instead of the full distribution. As shown in Figures 5 and A1, every dataset collected from the monitored system was slightly different. The operation of azimuth thrusters, like many other industrial machines, is dynamic and the precise identification of the system based on a fixed training set is challenging. Therefore, it could be useful to update the models periodically based on the latest samples or by considering the samples from the earliest history to the current date, as was presented here, in Section 3. Such model maintenance, however, requires that the undamaged health state is ensured during the period covered by the training data, perhaps with additional methods. The versioning and control of files, databases, metadata and their relations require meticulous planning if the models are updated regularly. However, this should be feasible if the models are kept simple, as in Equation (1), and the number of features is low and their calculation is fast, as demonstrated in Section 3.4. To further improve the training subset selection, it could be useful to study the shaft speed ranges where the deviation from the normal operation can be found the most effectively in the case of different faults. For example, the varying RMSE values in different speed ranges in Figure 10 illustrated that the level of change required for anomaly detection is varying. As illustrated in Figure 7, the typical variation of a vibration feature is sometimes larger on the high shaft speed (>400 rpm), and then, the absolute change in the feature value must be large to detect the anomaly. In some cases, it could be easier to detect the change in low-speed regions due to the lower typical variation in feature values, but the fault symptoms in low rotational speed may be faint [38]. Therefore, the research on signal processing to increase the sensitivity to varying faults could further benefit practical application and progress towards automated CM algorithms.