# On Training Data Selection in Condition Monitoring Applications--Case Azimuth Thrusters
###### Abstract  
Machine learning techniques are commonly used in the vibration-based condition monitoring of rotating machines. However, few research studies have focused on model training from a practical viewpoint, namely, how to select representative training samples and operating areas for monitoring applications. We focus on these aspects by studying training sets with varying sizes and distributions, including their effects on the models to be identified. The analysis is based on acceleration and shaft speed data available from an azimuth thruster of a catamaran crane vessel. The considered machine learning algorithm was previously introduced in another study suggesting it could detect defects on the thruster driveline components. In this work, practical guidance is provided to facilitate its implementation, and furthermore, an adaptive method for training subset selection is proposed. Results show that the proposed method enabled the identification of usable training subsets in general, while the success of the previous approach was case-dependent. In addition, the use of Kolmogorov-Smirnov or Anderson-Darling tests for normal distribution, as a part of the method, enabled selections that covered the operating area broadly, while other tests were unfavorable in this regard. Overall, the study demonstrates that reconfigurable and automated model implementations could be achievable with minor effort. acceleration; anomaly detection; automation; azimuth thruster; system identification; training data +
Footnote †: journal: _Applied_  
training data is usually conducted as a pre-processing step [4, 5] and it often requires many trials to achieve a usable model. In common practice, the sample selection equals the elimination of samples that are noisy, redundant, or irrelevant in available datasets, and it is often realized through a manual procedure. Additionally, automated feature selection and dimensionality reduction techniques are applied to reduce the data dimensions, namely, the number of predictor variables to ease the learning process [12]. The importance of automated data selection is increasingly noted because firstly, the volume of data is expected to grow in modern computational applications [1, 13] and secondly, large datasets tend to increase information redundancy and complexity, leading to low prediction precision and to decreased computational efficiency in data-driven modeling [9, 14]. The recent literature on monitoring of rotating machines based on ML methods is commonly focused on the prediction performance of classifiers [3, 15], but the reasoning for training data selection remains unclear. However, it is often mentioned that the training samples should enable the most accurate generalization [16]. In addition, they should be sampled from a broad distribution across the input space to contain the natural variability of the quantities in use [9]. On the other hand, all the data from such a distribution are not equally useful in model training [17] and random selection is then impractical. In industrial applications, training data are typically hand-picked based on the status codes given by the monitoring system [4, 12] and based on expert knowledge [6]. The fault data of rotating machines, collected into data repositories, typically have drawbacks such as the narrow coverage of the complete operating area or poor quality. Moreover, each unique fault may show new, previously unseen symptoms in indirect measurements, such as acceleration signals. Therefore, the data of fault-free operating conditions solely are commonly applied for model training [4, 5, 6, 12, 18]. The one-class classifier is then used for anomaly detection. However, the problem of training data selection is mainly approached from the perspective of maximizing the generalization performance in classification [19] by finding the smallest set of samples [8] with the assumption that representative data are available in more than one class.