AI-Based Predictive Maintenance in Industrial Systems
This paper describes a novel AI-driven predictive maintenance approach in the context of industrial systems that overcomes the limitations of the existing approaches including high dependency on the availability of data, model complexity and generalisation. With the integration of transformer-based structures, hybrid learning, edge AI optimization and adaptive transfer learning, the proposed system improves fault prediction accuracy, reduces latency and enables real-time deployment. The integration with digital twin and federated learning also makes scalability and data privacy guaranteed. Experimental results reveal 23% increased accuracy in detecting faults and 30% decreased unplanned downtimes, which demonstrate the robustness in adaptation across a wide range of industrial environments.