Abstract
Maintenance has always been a key activity in the manufacturing industry because of its economic consequences. Nowadays, its importance is increasing thanks to the “Industry 4.0” or “fourth industrial revolution”. There are more and more complex systems to maintain, and maintenance management must gain efficiency and effectiveness in order to keep all these devices in proper conditions. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, even though often these programs are complex to manage and understand. For this reason, several research papers propose approaches that are as simple as possible and can be understood by users and modified by experts. In this context, this paper focuses on CBM optimization in an industrial environment, with the objective of determining the optimal values of preventive intervention limits for equipment under corrective and preventive maintenance cost criteria. In this work, a cost-benefit mathematical model is developed. It considers the evolution in quality and production speed, along with condition based, corrective and preventive maintenance. The cost-benefit optimization is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case.
Highlights
The connected industry is currently a fact [1]
Overall, this paper focuses on the problem of the Condition-Based Maintenance (CBM) optimization in a manufacturing environment, with the aim of determining the optimal deterioration levels beyond which Predictive Maintenance (PM) activities should be applied under cost and profit criteria
Maintenance costs of equipment come from condition monitoring (CM), preventive maintenance, corrective maintenance, and costs of upgrading or substitution of components (Cu )
Summary
The connected industry is currently a fact [1]. Thanks to the explosion of the Industry 4.0 which promotes automation through computer systems in manufacturing and aims to achieve an intelligent or smart factory [2], Condition-Based Maintenance (CBM) and Predictive Maintenance (PM) have gained importance in the last few years. The recent literature concerning these terms is properly summarized in specific bibliographic reviews [6,7,8] These studies promote implementation frameworks for CBM such as the one proposed in reference [4], which are appropriate for complex production systems based on data mining and machine learning. Azadeh et al [11] maintain that most existing literature either discusses CBM optimization of single component systems or focuses on technical issues about condition monitoring equipment and diagnosis. In this approach, a multi-component system will be approached.
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