Advancing Predictive Maintenance Research Trends: Using Artificial Intelligence for Enhanced Industrial Reliability

The rise of Industry 4.0 technologies has underscored the necessity of predictive maintenance (PdM) to sustain industrial systems' reliability and cost-effectiveness. As industries increasingly rely on complex machinery and interconnected systems, the traditional reactive maintenance approach is proving inadequate in minimizing downtime and optimizing resource allocation. This study offers a comprehensive examination of current Artificial Intelligence (AI)-driven predictive maintenance trends, highlighting their transformative impact on industrial operations. By analyzing datasets from diverse sectors-including the rail industry, manufacturing, aerospace, Industry 4.0 environments, and cloud-based systems-this study demonstrates how advanced machine learning algorithms, deep learning models, and the Internet of Things (IoT) are revolutionizing PdM practices. These methodologies enable real-time data analysis, early fault detection, and precise maintenance scheduling, leading to substantial cost savings, enhanced operational efficiency, equipment longevity, and safety. The results indicate that models such as Convolutional Neural Network (CNN) and Gradient Boosting Machine (GBM) achieved accuracy rates of up to approximately 95%, significantly outperforming traditional approaches. However, despite the significant progress, challenges such as data quality, model interpretability, and implementation costs remain prevalent. This study is the first to comprehensively examine deep analysis of these trends, identifying gaps in current research and proposing future directions for more robust PdM frameworks.