Mantenimiento Predictivo
Detecte Antes de que Falle
Mantenimiento predictivo impulsado por IA con análisis de gemelos digitales — análisis de vida Weibull, análisis RCFA y detección proactiva de fallas.
Key Capabilities
- Análisis de Fiabilidad Weibull
Modelos estadísticos de predicción de vida útil basados en tiempo de operación, historial de mantenimiento y condiciones ambientales.
- Detección de Anomalías con IA
Fusión de sensores con filtro Kalman y modelos ML que identifican patrones de desviación de datos de operación normales.
- Análisis de Causa Raíz
Flujos RCFA estructurados que mapean síntomas de falla a causas raíz y recomiendan acciones correctivas.
- Integración de Órdenes de Trabajo
Creación automática de órdenes de trabajo desde alertas predictivas a través de Inspector, rastreando desde detección hasta reparación y verificación.
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 DatosMesh?
| Traditional CMMS | DatosMesh 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
Resultados Típicos
| Métrica | Impacto | Fuente |
|---|---|---|
| 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 |
Preguntas frecuentes
La implementación básica requiere tiempo de operación de equipos y registros de mantenimiento. El análisis avanzado se beneficia de datos de vibración, temperatura, corriente y presión.