Optimizing Smart Manufacturing Through Multi-Agent Integration of Machine Learning Models: A Conceptual Framework

In the era of Industry 4.0, predictive maintenance (PdM) has become crucial for sustainable manufacturing, transforming machine maintenance through digital solutions. Despite challenges in organization, finance and data management, PdM significantly reduces machine downtime and costs, extends equipment life and enhances production efficiency by following a precise workflow from data collection to decision-making. This paper introduces an innovative multi-agent-based framework that aims to transform PdM in manufacturing by fostering collaborative learning among machine learning (ML) models. Employing a multi-agent systems (MASs) approach, the framework enhances multiple ML models' integration, coordination and optimization to facilitate adaptive decision-making. The framework is specifically applied to hydraulic systems within the Oil and Gas industry (OGI), where MASs and ML have been underexplored. The proposed approach improves system responsiveness and decision support, leading to smarter, more proactive maintenance strategies. A real-world case study demonstrates the framework's effectiveness in reducing downtime and optimizing operational processes, providing a significant step towards smarter manufacturing practices. This research sets a benchmark for future advancements in PdM, particularly in critical industrial sectors like OGI.