# On Training Data Selection in Condition Monitoring Applications--Case Azimuth Thrusters
###### Abstract
The selection strategies include the pruning of redundant samples [9], evolutionary computation methods [10], methods combining feature selection and data sampling [16] and experimental design techniques [20, 21]. In addition, various studies focus on sample selection for specific algorithms, such as support vector machines [9, 22, 23], decision tree classifiers [16], nearest neighbor classifiers [19] and neural networks [20]. Such methods are beneficial for specific training sets containing representative data of classes. By contrast, this study focuses on selecting the training subsets that define representative operating areas for condition monitoring based on one class instead of trying to maximize the classification performance. The operating areas are defined by a single variable, namely, the shaft rotational speed, because it is commonly available in rotating machines including azimuth thrusters. This approach enables the rejection of unfavorable operating areas for monitoring, and furthermore, the selection process is unaffected by the case-specific fault conditions, which improves its general applicability. Furthermore, industrial systems typically change during operation, which causes a dataset shift [24]. The detection of shifted datasets [25, 26] and adaptation to them [27, 28] are general goals in ML development that aim at practical implementation. Therefore, the data selection procedure is analyzed in this study with multiple datasets collected in chronological order during operation. The study was made by applying the probabilistic condition monitoring algorithm introduced in [6] on the datasets collected from a single azimuth thruster. While the previous work [6] was focused on describing the algorithm for anomaly detection, this work clarifies the effect of training data on the system identification procedure of the algorithm. The performance is studied based on relatively large datasets which were not available in the previous work. More specifically, the main contributions of the study can be summarized as follows. The effects of training data on the automatically selected training subsets are illustrated, and the model adaptation to different datasets is analyzed. The 'training subset selection' process of the algorithm is analyzed by using the original approach for dataset segmentation, and a new, more flexible approach suitable for large datasets with varying distributions is presented. The automated selection of training subsets is based on identifying the shaft speed ranges that exhibit normal distribution in the regression residuals of predicted features. Therefore, various hypothesis tests, such as Kolmogorov--Smirnov (KS) [29], Anderson-Darling (AD) [30], Lilliefors' test [31], Jarque-Bera (JB) [32] and Shapiro-Wilk (SW) [33], are also compared as a part of the selection process. The case study provides guidance for the implementation of the considered condition monitoring algorithm. In addition, the approach to training data selection with the purpose of identifying the operating areas in place of maximizing the classification performance gives a practical viewpoint to the use of automated supervised learning in industrial condition monitoring. The remainder of the paper is organized as follows. Section 2 presents the procedures applied in the training data selection and introduces the data. Section 3 presents the results and Section 4 discusses their relevance to practical applications. Finally, Section 5 concludes the findings.