Sacmi/CDS Use Case

Summary

New machinery is equipped with a vast range of sensors, thus making the latter generation of production equipment and advanced CPSs. However, given the high CAPEX of production equipment, the majority of the already deployed machinery in existing shop floors is some years old. This other type of machinery is equipped with a limited number of sensors associated with fixed alarms. In this regard, these systems lack a pervasive condition monitoring system that can detect deterioration trends leading to failures, which some of them cannot be detected with existing alarm configuration and the operation strategy executed by the final user. Hence, an end-to-end PdM suite can provide final users with a solution for improving efficiency and equipment effectiveness.

Sensor data is gathered by additional sensors and a condition monitoring solution in the system and it is distributed in the Predictive Components to create outputs based on dedicated algorithms able to determine the remaining useful life and machine failure.

Z-BRE4K solution will have a positive impact on plant productivity and also component’s management, including inventory costs for spare components.
 

Results type(s)
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Structured mapping
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Comment:

SACMI-CDS found out the importance of collaboration not only with a mechanical engineering/maintenance-related professionals but also with different technical background experts that together can improve multi-tasking and combining shopfloor and office-related activities as well as scheduling of activities during the work journey.

In general, after the solution implementation (TRL5), testing the system on the shop floor (TRL6) and validation of the Z-BRE4K solution (TRL7) at end users, the very final lesson learnt can be summarised as follows:

  • Live data are gathered by sensors and other systems.
  • Data from individual data systems incorporated in a distributed system.
  • Quality and maintenance measurements are available.
  • Manual maintenance schedules are replaced with PdM procedures and schedules.
  • Maintenance experts supported by gathered data and predictions to improve their know-how in the maintenance domain.
  • PdM accuracy and performance is established.
  • Productivity improved.
  • Cost reduction obtained.
  • Possibility of Testing a full end-to-end solution for maintenance management including prescriptive and predictive analytics.