Rotating equipment risk detection
Detect early degradation patterns across pumps, compressors, motors, and other critical assets before they become failures.
← Solutions
Predictive Maintenance
Predictive Maintenance for Industrial Operations
Move from alarm-heavy maintenance to AI-guided predictive maintenance with industrial sensing, digital twins, and closed-loop execution.
Key Capabilities
Core building blocks that define how this page delivers operational value.
- Multi-source sensing and context fusion
Combine vibration, temperature, process, historian, and asset context through Data Fusion Services so each diagnosis starts with real operational context.
- AI anomaly detection and health scoring
Use FactVerse AI Agent to distinguish emerging degradation from normal operating variance and reduce false alarms.
- Twin-based diagnostics and validation
Review equipment state inside the twin, compare asset relationships, and validate decisions before dispatching work.
- Closed-loop maintenance execution
Move from detection to work order, field action, and verification through Inspector and connected maintenance workflows.
Use Cases
Practical applications and proven success scenarios across industries.
Cross-system risk correlation
Correlate sensor signals, process context, and asset relationships to surface maintenance priorities earlier.
From alert to completed work order
Connect anomaly review, maintenance planning, field execution, and verification in one operational loop.
From reactive maintenance to verifiable decisions
PdM gives operations teams a decision loop instead of a wall of alarms. By combining industrial sensing, asset context, AI analysis, and digital twins, teams can identify what is changing, understand why it matters, and act before downtime happens.
Signal → Analysis → Simulation → Decision
- Signal ingestion — Data Fusion Services brings together sensor streams, historian tags, inspection records, and equipment metadata.
- AI analysis — FactVerse AI Agent evaluates degradation patterns, health signals, and anomaly trends.
- Twin validation — FactVerse Twin Engine and FactVerse provide spatial and operational context for diagnosis.
- Execution — Inspector turns validated findings into work orders, field tasks, and traceable follow-through.
Operational validation for maintenance teams
PdM combines trusted sensing, asset context, AI analysis, and twin-based review so teams can evaluate maintenance risk with more context and less guesswork.
- trusted industrial sensing at the edge
- multi-source operational context
- AI-driven trend analysis and health evaluation
- digital twin visibility for maintenance decisions
Earlier intervention, lower noise
Instead of reacting only after threshold alarms fire, teams can review emerging issues in context, prioritize the right assets, and move into planned action with less noise.
Related Products
- FactVerse — platform layer for operational context
- FactVerse AI Agent — anomaly detection, analysis, and decision support
- FactVerse Twin Engine — twin validation and execution context
- Data Fusion Services — connectivity for sensors, historians, and systems
- Inspector — work orders and field execution
Typical outcomes
| Metric | Impact |
|---|---|
| Earlier signal review | Faster identification and prioritization of emerging maintenance issues |
| Unplanned downtime | Lower through earlier intervention and planned maintenance |
| False alarms | Reduced through trend-based analysis and contextual diagnostics |
| Maintenance execution | Faster handoff from detection to validated field action |
Frequently Asked Questions
Typical starting points include vibration, temperature, current, pressure, historian tags, inspection records, and equipment metadata. Data Fusion Services connects them into one operational model.
Thresholds react after a limit is crossed. PdM evaluates trends, equipment behavior, and operational context to surface earlier and more trustworthy warnings.
Yes. Inspector and connected APIs can route detections into existing work order and maintenance systems so teams do not need to replace their current maintenance stack.