| Resumo: | In recent years manufacturing companies have been facing a major shift in the manufacturing requirements, for example the shift in demand for highly customized products resulting in a shorter product life cycle, rather than the traditional mass production of standardized products. As a consequence of the change, the enterprises are facing the need to adapt, forcing all sectors of the manufacturing activity to move accordingly. Maintenance is one of the major activities in manufacturing as it highly influences production productivity and quality, and has a direct impact on production cost and customer satisfaction. Nowadays, corrective and scheduled maintenance are widely implemented. However, the manufacturing world need to adapt to this new reality by implementing new, intelligent and innovative maintenance systems capable of predicting in advance possible failures. Lately, predictive maintenance systems and tools have been developed and continue to be studied and improved. However, companies do not have enough trust on these systems to fully rely on them. Considering all these aspects, the work developed on this thesis introduces a system architecture for an intelligent predictive maintenance system based on the Condition-Based Maintenance (CBM) to be used in the Catraport case study, focusing particularly on the development of the monitoring module of the system architecture. This module comprises a tool developed by using Node-RED that displays the collected data alongside with the warnings triggered by cross-checking the incoming data with implemented decision rules, through the use of graphics and text. Additionally, an Android mobile application was also developed to allow consulting remotely the operating state of the assets.
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