ANALYSIS OF SENSOR INDICATORS FOR PREDICTIVE MAINTENANCE OF HEAVY INDUSTRY PROCESS LINES

Authors

DOI:

https://doi.org/10.32689/maup.it.2025.1.3

Keywords:

process line, predictive maintenance, condition-based predictive maintenance, predictive model, descriptive statistics, contextual statistics, correlation analysis

Abstract

The article considers the directions of digital transformation in the maintenance of process lines of heavy industry, aimed at predicting potential equipment malfunctions before their complete functional failure. The choice of methods for detecting problematic data coming from sensors of the process line is justified, aimed at reducing the time interval between the occurrence of a problem and its detection. Approaches to ensuring optimal updates of Predictive Maintenance models are considered, which enhance the accuracy of failure prediction. The purpose of the article is to research and identify effective methods for analyzing the quality of data from sensors of process lines in heavy industry to increase the accuracy of failure prediction in predictive maintenance. Methodology. Software and hardware solutions aimed at increasing the accuracy of forecasting faults in process lines based on the RCM methodology have been analyzed. Methods of correlation analysis, descriptive and contextual statistics were used to monitor sensor indicator data. A sequence of stages for detecting problematic data in real-world production conditions in heavy industry is proposed, which provide automated generation of potential sources of faults. The scientific novelty of the research lies in the identification of methods and approaches to analyzing the performance of sensors of process lines in the conditions of Industry 4.0, aimed at increasing the accuracy of predictive maintenance models. Conclusions. Maintaining Predictive Maintenance models that serve heavy industry process lines in real production conditions requires adjusting and updating their parameters based on constant monitoring of the equipment status in real time. To ensure the detection of problematic data, the feasibility of developing an information system, implementing the analysis of sensor indicators data using descriptive statistics, contextual statistics, and correlation analysis methods. The results of implementing the developed information and analytical system, which includes a recommendation system at the stage of forming a data set for analysis, were accompanied by an increase in failure prediction accuracy and a decrease in the time between the occurrence and detection of problems leading to equipment failures. This indicates that operational tasks are being solved with higher accuracy and efficiency.

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Published

2025-05-28

How to Cite

БОЛЮБАШ, Н., & ФІНЬКОВА, О. (2025). ANALYSIS OF SENSOR INDICATORS FOR PREDICTIVE MAINTENANCE OF HEAVY INDUSTRY PROCESS LINES. Information Technology and Society, (1 (16), 23-40. https://doi.org/10.32689/maup.it.2025.1.3