Predictive maintenance refers to predicting malfunctions using data from monitoring equipment and process performance measurements. Machine learning algorithms and techniques are often used to analyze equipment monitoring data. Machine learning is the process in which a computer can work more precisely by collecting and analyzing data. It is often the case that machine learning algorithms use supervised learning, in which labelled data is used to feed the algorithm. However, there are many supervised machine learning algorithms available. Therefore, choosing the best-supervised machine learning algorithm to resolve predictive maintenance issues is not trivial.

This paper aims to increase the performance of predictive maintenance and achieve its goals by selecting the most suitable supervised machine learning algorithm.

Based on the most commonly used criteria in research articles, we selected three supervised machine learning algorithms from a comparative study: Random forest, Decision tree and KNN. We then tested selected algorithms on data from real-world and simulation scenarios. Finally, we conducted the experiment based on vibration analysis and reliability evaluation. We noticed that Random forests and Decision trees obtained slightly the same performance. KNN is a better classification algorithm for extensive volumes of data; on the contrary, Random forest performs better in the case of small datasets.