Abstract
In traditional industrial settings, maintenance methods based on reactive repairs or scheduled time intervals often lead to downtime and reduced efficiency. Therefore, predictive maintenance uses data to predict failures before they actually occur. The objective of this study is to develop an intelligent predictive maintenance system based on machine learning and implement it in the industry of Kazakhstan, focusing on the principles of Industry 4.0. The system is based on collecting data from sensors (current, temperature, pressure, vibration) using Siemens PLCs, integrated via the OPC UA protocol into the industrial IoT infrastructure. Additionally, computer vision is used to monitor the equipment condition in real time. The obtained data is processed by machine learning algorithms, including neural networks, linear regression, and autoencoders. To adapt the model to changes, it is trained continuously using Bayesian updating. Visualization and interaction with users are implemented via SCADA for engineers and Power BI for managers. In addition, the paper discusses the challenges associated with the deployment of predictive maintenance solutions and suggests future directions for improving scalability, security, and real-time data processing capabilities. The obtained results contribute to the growing body of research in the field of predictive maintenance, demonstrating its potential to improve efficiency, reduce operating costs, and support the transition to datadriven, intelligent manufacturing systems. The work demonstrates the potential of predictive maintenance as a solution for aging industries with a shortage of engineering personnel and a step towards digitalization within the framework of Industry 4.0.