MonoM Predictive Maintenance
MonoM Predictive Maintenance is an industrial AI platform designed to unify operational technology (OT) and information technology (IT) data, delivering accurate predictions and prioritized actions to prevent equipment failures. By integrating existing sensors, historians, and computerized maintenance management systems (CMMS), MonoM provides a comprehensive view of asset health without the need for hardware replacements.
Key Features and Functionality:
- Comprehensive Asset Coverage: Utilizes hybrid models combining physics and AI to monitor various asset types, including rotating, static, electrical, and process equipment.
- Contextualized Predictions: Fuses condition data (e.g., vibration, thermography) with OT data (e.g., SCADA, historians) and enterprise resource planning (ERP) systems to eliminate false positives and provide actionable alerts.
- Rapid Deployment: Integrates seamlessly with existing infrastructure, enabling deployment within weeks and immediate realization of benefits.
Primary Value and Problem Solved:
MonoM addresses the challenges of fragmented predictive maintenance systems that often result in false alarms and lack context. By unifying disparate data sources and applying advanced analytics, it reduces non-actionable alerts by 85%, accelerates time-to-action by 60%, and decreases reactive maintenance by 30%. This leads to increased equipment availability, extended maintenance planning horizons, and overall reduction in maintenance costs, transforming maintenance operations into a strategic advantage.
