Application of machine learning algorithms for failure prediction and adaptive control of industrial systems

Abstract

Application of machine learning algorithms for failure prediction and adaptive control of industrial systems

Skvorcov A.A., Anureva M.S., Solodovnikov A.N.

Incoming article date: 24.02.2025

The article focuses on the application of machine learning methods for predicting failures in industrial equipment. A review of modern approaches such as Random Forest, SVM, and XGBoost is presented, with emphasis on their accuracy, robustness, and suitability for engineering tasks. Based on the analysis of real-world data (temperature, pressure, vibration, humidity), models were trained and compared, with XGBoost demonstrating the best performance. Key parameters influencing failures were identified, and a recommendation system was proposed, combining statistical analysis and predictive modeling. The developed solution enables timely detection of failure risks and optimization of maintenance processes.

Keywords: machine learning, predictive modeling, equipment management, failure prediction, data analysis