Stop reacting to failures — start preventing them
Traditional maintenance operates in two modes: run-to-failure (expensive emergencies) or calendar-based replacement (wasteful over-maintenance). Digital twin-powered predictive maintenance introduces a third option: service exactly when needed, based on actual equipment condition tracked through the FactVerse platform.
From sensor data to maintenance decisions
The digital twin integrates live sensor feeds with equipment models through DFS. FactVerse AI Agent spots patterns across multiple data streams that are difficult for operators to detect manually — correlating signals across subsystems to identify emerging issues before they become failures.
Every asset tells a story — if you're listening
Equipment doesn't fail suddenly. It degrades gradually — a bearing losing tolerance, a motor drawing slightly more current, a heat exchanger fouling over months. Performance tracking in the digital twin captures these trends, making degradation visible before it becomes failure.
Maintenance that improves over time
Every intervention and its outcome feeds back into the system. Teams build a maintenance knowledge base that captures what worked, improving diagnosis accuracy and repair quality across the organization.
Why 데이터Mesh?
| Traditional CMMS | 데이터Mesh Approach |
|---|---|
| Equipment-centric work orders | Spatial digital twin context — see where issues are |
| Calendar-based maintenance schedules | AI-driven prediction using Weibull reliability + Kalman filters |
| Manual inspections | AR-guided procedures via Director |
| Separate analytics tools | Integrated simulation within the twin — test maintenance strategies virtually |
| Isolated maintenance records | Closed-loop: detect → work order → dispatch → execute → verify |
Related Products
- FactVerse — Digital twin platform
- FactVerse AI Agent — Anomaly detection & analytics
- Inspector — Work order management & field execution
- DFS — Sensor and system data connectivity
일반적인 성과
| 지표 | 효과 | 출처 |
|---|---|---|
| Unplanned downtime | ↓ 30–50% with AI prediction | Weibull + ML anomaly detection |
| Maintenance cost | ↓ 20–30% vs. reactive maintenance | Condition-based vs. run-to-failure |
| Equipment lifespan | ↑ 15–25% with optimized maintenance | Right intervention at right time |
| Spare parts inventory | ↓ 20% with demand forecasting | AI predicts what's needed when |
| Mean time to repair (MTTR) | ↓ 40% with AR-guided procedures | Inspector + Director combined workflow |
자주 묻는 질문
DFS는 OPC UA, BACnet, REST API 등 표준 프로토콜로 기존 시스템에 연결됩니다.