Application of AI-based Predictive Maintenance for Industrial Processes

Modern industry is increasingly relying on digital technologies to optimize maintenance processes, reduce costs and ultimately increase productivity. Conventional maintenance models, such as corrective and preventive maintenance, often lead to unnecessary downtime and high operating costs. Predictive Maintenance (PdM), which is based on data analysis and artificial intelligence (AI), enables the timely detection of failures and the optimization of maintenance cycles and is therefore a key component of modern industry. With the advancement of artificial intelligence (AI) and machine learning, data analytics can accurately predict failures, thereby reducing the need for preventive and corrective maintenance in the form that was common before the application of AI. Predictive maintenance (PdM) is emerging as a key element of modern industry, enabling a significant reduction in downtime, an increase in operational efficiency and a reduction in maintenance costs. This paper explores the application of artificial intelligence, including machine learning (ML), deep learning (DL) and the Internet of Things (IoT) in predictive industrial maintenance, analyzes the key implementation challenges in implementation, considers the potential benefits for industrial systems and discusses the challenges and prospects for the further development of this approach.