Apriori-Enhanced Transformer with Federated Graph Neural Networks for Real-Time Dairy Process Optimization and Predictive Maintenance

Abstract

The modernization of dairy production demands intelligent, adaptive systems capable of optimizing workflows, predicting equipment failures, and minimizing human intervention. This paper proposes a Hybrid Deep Rule-Based Learning Framework that integrates Apriori algorithm-enhanced Transformer architectures for dynamic rule extraction and real-time process optimization. The system adaptively learns operational rules from heterogeneous dairy workflows while leveraging Federated Graph Neural Networks (GNNs) to analyze machine interdependencies across distributed production units. This decentralized approach ensures data privacy while enabling predictive maintenance, fault localization, and efficient resource allocation. Experimental results across multiple dairy plant simulations demonstrate a workflow optimization accuracy of 98.1%, significantly reducing downtime and enhancing overall yield. The proposed framework represents a scalable, intelligent automation solution for smart dairy manufacturing environments, ensuring real-time adaptability, enhanced decision-making, and reduced reliance on manual oversight.