Bowden, DavidDavidBowdenMarguglio, AngeloAngeloMarguglioMorabito, LucreziaLucreziaMorabitoNapione, ChiaraChiaraNapionePanicucci, SimoneSimonePanicucciNikolakis, NikolaosNikolaosNikolakisMakris, SotirisSotirisMakrisCoppo, GuidoGuidoCoppoAndolina, SalvatoreSalvatoreAndolinaMacii, AlbertoAlbertoMaciiMacii, EnricoEnricoMaciiO'Mahony, NiamhNiamhO'MahonyBecker, PaulPaulBeckerJung, SvenSvenJung2022-03-142022-03-142019https://publica.fraunhofer.de/handle/publica/404521Data management and processing to enable predictive analytics in cyber physical systems, holds the promise of creating insight into the underlying processes, discovering criticalities and predicting imminent problems. Hence, proactive strategies can be adopted, with respect to predictive analytics. This paper discusses the design and prototype implementation of a plug-n-play end-to-end cloud architecture, enabling predictive maintenance of industrial equipment. This is enabled by integrating edge gateways, data stores at both the edge and the cloud, and various applications, such as predictive analytics, visualization and scheduling, integrated as services in the cloud system. The proposed approach has been implemented into a prototype and tested in an industrial use case related to the maintenance of a robotic arm.enpredictive maintenanceedge computingcloud architecturesmart data acquisition658670conference paper