In particular, the SERENA system foresees to:

  • gather and process data from different devices and sensors within the factory by integrating a smart data collection device;
  • distinguish the ‘smart data’ from the ‘big data’ considering edge computing methods;
  • apply advanced data analyticsAI methods and hybrid methods considering physics model and data driven approaches for predicting potential failures and improve process related parameters;
  • allow remote access and data processing in cloud for predicting maintenance actions;
  • enable easy-to-use interfaces for managing data and providing human operator support for machines status and maintenance guidance using AR devices;
  • fully demonstrate in different applications (white goods, metrological engineering and elevators production) and investigate applicability in steel parts production industry (extended-demonstration activities) checking the link to other industries (automotive, aerospace etc.) showing the versatile character of the project.


SERENA will provide a bridge for transferring the latest R&D results in predictive maintenance towards in- herently different industrial sectors considering the needs for versatility, transferability, remote monitoring & control, by providing:

  • advanced IoT systems and smart devices for collecting data from different resources (robots, ma- chines, welding guns, PLCs, exter- nal sensors etc.) and cloud-based remote management of these data
  •  platform for predictive maintenance activities & AR based operator local maintenance personnel support,
  • advanced artificial intelligence methods for predictive mainte- nance,
  •  plug-and-play cloud-based com- munication framework.

SERENA represents a powerful plat- form to aid manufacturers in simplify- ing their maintenance burdens, by re- ducing costs, time and improving the productivity of their production pro- cesses. 


SERENA’s expected impact:

  • 10% increased in-service efficiency through reduced failures rates, downtime due to repair, unplanned plant/production system outages and extension of component life.
  • More widespread adoption of predictive maintenance as a result of the demonstration of more accurate, secure and trustworthy techniques at component, machine and system level.
  • Increased accident mitigation capability.

Pilot Cases