Extracting Knowledge about Production Process Execution through Analysis of Cutting Machine Motor Current

Abstract

This paper presents a multi-stage study focused on analyzing current data from sensors installed in cutting machine motors. The collected data serves as a foundation for monitoring machine performance, diagnosing anomalies, determining efficiency, and identifying the machines' cutting direction. For this purpose, VBA-based applications were developed, which operate on data retrieved from a database server. Additionally, exploratory analyses were carried out in the R programming environment. The findings demonstrate that analyzing energy-related data can be a valuable source of operational knowledge and can support decision-making within the Industry 4.0 paradigm. The results obtained have significant practical implications. Firstly, they enable real-time monitoring of machine operations and allow for rapid responses to irregularities. Secondly, historical data becomes a knowledge base for maintenance planning, work reorganization, and evaluating operator performance. Thirdly, the ability to determine the cutting direction based on motor current readings creates opportunities for developing semi-autonomous control systems for mining machines. The study also includes an analysis aimed at extracting knowledge for automatic classification of machine operating states, which may serve as a basis for generating reports based on recorded data. This knowledge can, in turn, support the verification or correction of event logs.