Design and Application of Predictive Maintenance Architecture for Electromechanical Systems for Industry 4.0

This paper investigates the development and implementation of a predictive maintenance (PdM) framework specifically engineered for electromechanical assets within the Industry 4.0 landscape. As modern production becomes increasingly autonomous and interconnected, the capacity to preemptively identify equipment malfunctions has become a cornerstone for optimizing operational uptime and efficiency. We propose an integrated PdM system that synthesizes sensor technologies, data analytics, and machine learning (ML) for continuous, real-time health assessment of electromechanical components. The proposed methodology encompasses a multi-stage process: diverse data acquisition, signal preprocessing, feature engineering, and the deployment of ML models alongside a decision-support tool for maintenance planning. To verify its efficacy, the architecture was applied to a case study involving industrial robotics in a manufacturing setting. Empirical results indicate that our approach significantly enhances fault prediction precision while simultaneously lowering maintenance expenditures. Additionally, the study addresses critical deployment factors such as data protection, system interoperability, and scalability. These findings offer both theoretical insights and a practical roadmap for adopting PdM strategies in smart industrial environments.