Abstract
The large amount of historical data contains information about events that occur along an industrial production line. Having a set of historical data about emergency events and their causes makes it possible to automate decision-making processes based on a data-driven approach. Data-driven approaches, particularly machine learning (ML), are attracting attention. Due to its visualization and interpretability characteristics, the decision tree (DT) model is an important ML tool for decision analysis. This paper aims to present the possibility of using DT to increase the efficiency and effectiveness of maintenance activities by identifying the probable cause of failure based on historical data. Based on the research conducted, we have shown that the use of machine learning techniques can improve the accuracy of decisions regarding the type of maintenance work that should be carried out to efficiently and effectively remove failures and reduce losses caused by machine downtime.
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