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