Cost -Oriented Predictive Maintenance using Exponential Degradation Modelling: Application on Manufacturing Industries

Le résumé fourni par la source

In the era of Industry 4.0, characterized by the seamless integration of Internet of Things (IoT) and advanced analytics, Predictive Maintenance (PdM) emerges as a transformative strategy, addressing the limitations of reactive maintenance approaches. PdM is a condition-based maintenance strategy that relies on monitoring equipment in real-time, with the aim of executing maintenance actions precisely when required avoiding unnecessary preventive measures or unforeseen failures. This study intends to develop a comprehensive PdM methodology that integrates Remaining Useful Life (RUL) estimation and economic performance assessment within a novel framework. An evolutionary algorithm is proposed to model and optimize the maintenance schedule for industrial equipment based on exponential degradation patterns and a RUL-based decision support tool to determine the optimal timing for maintenance activities. This methodological approach not only extends the intervals between maintenance operations, but also evaluates and compares the cost-effectives of the suggested PdM strategy with the current manufacturing industries practices, in order to improve operation efficiency and minimize costly downtime. A key feature of this research work lies in its real-world applicability, as the effectiveness of the proposed framework is assessed within an actual manufacturing system. The findings of this study can provide valuable insights on the importance of intelligent, economic-oriented PdM strategies to improve the industrial environment in terms of costs, control and quality of production.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.