PREDICTIVE MAINTENANCE SYSTEM FOR WEAVING MACHINES BASED ON SCADA AND CONDITION MONITORING
Keywords:
predictive maintenance, weaving machine, SCADA, condition monitoring, remaining useful life, vibration trend analysis, OEE, bearing life, maintenance scheduling, WinCC.Abstract
This thesis presents the development of a predictive maintenance system for weaving machines based on continuous condition monitoring, SCADA data integration, and remaining useful life (RUL) estimation. Unlike reactive (breakdown-based) and time-based preventive maintenance strategies, the proposed system uses vibration trend analysis, statistical degradation modeling, and SCADA historian data to predict when each machine component will require service — before failure occurs. The system was validated on 12 Picanol Omni Plus 800 machines over 14 months. Results show that predictive maintenance scheduling reduced total maintenance costs by 41.2%, extended bearing service life utilization from 58% to 87% of rated life, eliminated 94.3% of unplanned breakdown stops, and improved overall equipment effectiveness (OEE) from 68.4% to 84.7%.
References
