Application of machine learning models for predictive maintenance in industrial production systems

In this study, analyzed the applicability of machine learning methods for predictive maintenance in industrial production systems based on multivariate sensor data. Investigated approaches for integrating condition monitoring data with supervised machine learning models to enable early detection of equipment degradation and failure risks. Studied data preprocessing techniques, feature extraction strategies, and time-series segmentation methods required to ensure robustness and stability of predictive maintenance models under industrial operating conditions. Determined the predictive performance of classical machine learning algorithms and deep learning models through comparative evaluation using benchmark and simulated industrial datasets. Established the impact of machine learning–based predictive maintenance on key operational indicators, including unplanned downtime, failure rate, maintenance costs, and equipment availability. Formed a methodological framework that links predictive model outputs with maintenance scheduling and decision support processes in production environments. Demonstrated that data-driven predictive maintenance can provide measurable improvements in operational efficiency and equipment reliability compared to traditional preventive maintenance strategies. Presented a scalable and adaptable analytical approach suitable for implementation in modern industrial production systems to support sustainable and efficient maintenance management.