Predictive maintenance has evolved from scheduled oil sampling to real-time physics-informed digital twins that continuously track thermal and mechanical stress across critical drives.
1. Physics-Informed Machine Learning
By combining first-principles thermodynamic and mechanical equations with real-time SCADA sensor streams, MacProtec digital twins predict equipment degradation weeks before alarm thresholds trigger.
2. Monitored Critical Equipment Assets
- Kiln Drive Assemblies & Support Rollers: Tracking skew adjustments, contact pressures, and journal bearing temperatures.
- Vertical Roller Mills (VRM): Monitoring hydraulic pressure oscillations, table vibration spectrums, and grinding segment wear profiles.
- High-Pressure Slurry Pumps: Detecting impeller cavitation and casing wall thinning in real time.
3. Operational Benefits & Reliability
- Zero unplanned catastrophic drive failures across monitored assets.
- Optimized maintenance turnaround schedules based on real asset health rather than arbitrary calendar intervals.
- Real-time performance dashboards accessible securely across desktop and mobile operations centers.