Development of AI-Driven Predictive Maintenance Models for Industrial Machinery

Abstract

Artificial intelligence has transformed industrial operations by enabling the prediction of machine failures before they occur, substantially reducing downtime and maintenance costs. AI-driven predictive maintenance integrates machine learning algorithms, sensor networks, and real time analytics to assess machinery performance and identify early signs of degradation. This review examines the evolution, architecture, and functioning of predictive maintenance systems that rely on artificial intelligence. Emphasis is placed on the roles of machine learning, deep learning, analytics platforms, and industrial Internet of Things (IIoT) infrastructures that support large-scale condition monitoring. The paper discusses data acquisition challenges, algorithmic performance issues, and the critical need for interpretability in industrial environments. Case studies from manufacturing, power systems, and transportation demonstrate how AI- enabled solutions outperform traditional maintenance strategies in accuracy and operational efficiency. The review also analyzes emerging trends, including digital twins, federated learning, and edge-AI implementations designed to enhance resilience and scalability in smart factories. The findings highlight that AI-driven predictive maintenance is central to the modernization of Industry 4.0 ecosystems and will continue to evolve as industries transition toward fully autonomous and self-optimizing systems.