Toward a Predictive Maintenance: Implementing an Innovative Maintenance System for Manufacturing Production Lines Based on New Technologies

The rise of “big data” has been profoundly transformed the landscape of the manufacturing sector, putting pressure on companies’ competitiveness and making the effective utilization of this vast data a critical imperative for efficient maintenance. Therefore, predictive maintenance plays an essential role in meeting these challenges, anticipating maintenance needs and enhancing equipment durability. This paper introduces a new dimension that considers not only the monitoring and prognosis of component failures but also the formulation of optimal and efficient decisions for maintenance planning and execution. The objective of the developed system is to include real-time data acquisition from various sensors through an IoT-based system, proactively detecting potential failures before they escalate into major issues, estimating the remaining useful life (RUL) of equipment using Deep Learning models, and ultimately making optimal and efficient decisions for maintenance planning and execution. This minimizes intervention costs and increases the productivity and efficiency of machines and systems, enhancing the company’s competitiveness, among other benefits. In this study, a real-world application was conducted to evaluate the proposed methodology, and several algorithms and AI techniques were introduced to suggest economical replacement methods. Based on the collected data, a new solution for prognosis is proposed using a regression model with the LSTM algorithm. The accuracy achieved by this model is 88.68%. The outdoor validation results obtained so far suggest that the developed method could be an efficient solution for industrial companies. As the proposed system performs prognosis data for each component, it develops an optimal maintenance scenario based on the ANN algorithm to optimize the maintenance plan and devise the most cost-effective and reliable strategies for equipment upkeep. The test loss and accuracy obtained by the decision model are respectively 0.0347 and 0.98. This approach opens up new perspectives for more efficient industrial maintenance management in the era of industry 4.0.