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Comment:
THe evalaution and assessment of the equipment condition through the predictive manitenance and data analytics of the SERENA project will move towards the preservation of the production equipment in normal workiong conditions ensuring high quality products.
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Comment:
SERENA solutions on predictive maintenance and maintenance-aware scheduling are expected to reduce the overall ratio of cost to perfromance by the on-time scheduling of maintenance operations with the minimum intervention to the production schedule.
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Comment:
The prediction of maintenance needs of the production equipment thorugh the predictive analytics and scheduling of the SERENA project is expected to reduce the defective workpieces caused by manufacturing equipments not in proper working condition.
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Comment:
Indutrial equipment not in proper working condition consumes greater quantities of input and operational sources than normal. The SERENA data-driven condition evaluation and prediction of potential failures will enabled the sustainability of the production machines to proper operational condition, thus contributing to reduced process resources.
SERENA aims towards the data-driven condition evaluation of machine and production equipment, which through machine learning techniques can provide insight in the remaining useful life of the equipment enabling the avoidance of production stops and thus reducing its overall costs. The combination of data-driven and physics based techniques is envisioned to increase the reliability of the prediction and contribute to a high perfromance production without undesired interruptions.