Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The Secure Integration for Predictive Maintenance (SIPM) framework represents a comprehensive solution for unifying AI-driven predictive maintenance capabilities with existing enterprise manufacturing infrastructure. This novel framework continuously processes sensor data, maintenance records, and production schedules through advanced time-series deep learning models to forecast equipment failures and optimize intervention timing. The system seamlessly integrates with enterprise resource planning and supervisory control and data acquisition systems, enabling automated work-order generation while minimizing operational disruption. Digital twin simulations validate the framework's effectiveness across diverse operational scenarios, demonstrating significant improvements in unplanned downtime reduction, maintenance cost optimization, and overall equipment effectiveness enhancement. The framework's architecture prioritizes interoperability and scalability, ensuring compatibility with heterogeneous manufacturing assets and enterprise IT environments. By coupling predictive insights with real-time operational intelligence, SIPM empowers manufacturing decision-makers to balance maintenance priorities against production commitments, resulting in improved throughput and product quality. The implementation showcases the transformative potential of tightly coupled AI-enterprise systems in optimizing resource allocation and production planning, establishing a foundation for next-generation intelligent, self-adaptive manufacturing ecosystems that position predictive maintenance as a strategic enabler for Industry 4.0 transformation.