###### Abstract  
Thanks to the digitalization of industry, maintenance is a trending topic. The amount of data available for analyses and optimizations in this field has increased considerably. In addition, there are more and more complex systems to maintain, and to keep all these devices in proper conditions, which requires maintenance management to gain efficiency and effectiveness. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, but often these programs are complex to manage and understand. The problem becomes more complex when equipment is analyzed in the context of a plant, where equipment can be more or less saturated, critical regarding quality, etc. Thus, this paper focuses on CBM optimization of a full industrial chain, with the objective of determining its optimal values of preventive intervention limits for equipment under economic criteria. It develops a mathematical plus discrete-event-simulation based model that takes the evolution in quality and production speed into consideration as well as condition based, corrective and preventive maintenance. The optimization process is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case, where the data gathered by the IoT (Internet of Things) devices at edge level can detect when some premises of the CBM model are no longer valid and request a new simulation. The simulation performed in a centralized way can thus obtain new optimal values who fit better to the actual system than the existing ones. Finally, these new optimal values can be transferred to the model whenever it is necessary. The approach developed has raised the interest of a partner of the Deusto Digital Industry Chair. Condition-Based Maintenance; Genetic Algorithms +
Footnote †: journal: Applied Soft  
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maintenance optimization, needs to consider several parameters that are not measurable by the same standard and respond to contradictory objectives. It is possible to over or under extend the operational lifetime of the equipment, by increasing or reducing preventive maintenance (PM) expenditures and thus preventing catastrophic failures, but without taking into account the overall plant profitability. Therefore, maintenance has a critical importance during production. Since maintenance requirements depend on a high amount of parameters, it is very delicate to regulate the optimal maintenance strategy that maximizes the profitability of the equipment. In addition, equipment focused long-term profitability is usually de-aligned with the short-term needs of directors, making decisions even more difficult. In recent years, many studies from different points of view have been carried out to find an optimal Condition Based Maintenance (CBM) policy. They were mostly focused on the optimization of single deteriorating equipment and did not consider either the configuration of the productive system or the influence of the whole system on each studied machine. Thus, although single equipment optimization methods might be useful in the case of productivity bottlenecks or continuous processes, they are less convenient in the case of machines working in multi-equipment manufacturing systems. Maintenance requirements of a single machine belonging to a multi-equipment system are directly determined by the amount of the stock of semi-elaborated products related to the machine as well as on the machine being a bottleneck or not. For example, the availability of a bottleneck machine will be critically important for the profitability of the company, whereas its failure will not have such a significant impact on the whole system (if stock levels and repair times are under control). Nevertheless, the recent literature (as summarized in specific reviews [6; 7; 8]) shows that the joint optimization of CBM activities of multi-equipment systems is still an area lacking sufficient research.