Predictive maintenance that stops failures before they stop production
Use intelligent digital twins to detect early signs of equipment degradation, predict failures in advance, and plan maintenance with confidence—without relying on guesswork or calendar-based schedules.
Why maintenance teams are stuck in reactive mode
Many organizations still rely on reactive repairs or time-based preventive maintenance. Traditional approaches focus on individual signals—but predictive maintenance changes the game by understanding how assets behave over time and in context.
The reactive reality
- Failures discovered too late to avoid downtime
- Alarms that trigger after damage has already occurred
- Maintenance performed too early—or too late
- Overtime, expediting, and spare-part firefighting
- Critical knowledge locked in a few experts
The predictive advantage
- Detect degradation before it becomes failure
- Plan maintenance based on real risk, not guesswork
- Understand asset behavior across different conditions
- Prevent repeat failures with intelligent insights
- Transform maintenance from reactive to strategic
From threshold alarms to real prediction
This solution applies the intelligent digital twin model to a specific operational challenge. For a full explanation of the model itself, see: What is an Intelligent Digital Twin →
With intelligent digital twins, predictive maintenance is not just about monitoring sensor values. It's about modeling how an asset should behave—and detecting when it starts to drift.
In predictive maintenance, the intelligent digital twin is used to:
- Represent the asset and its operating context
- Learn normal behavior under different loads and conditions
- Detect subtle changes that indicate early degradation
- Project how risk will evolve if nothing changes
This allows teams to move from "something is wrong" to "this asset is likely to fail in X days if we don't act."
A simple, practical flow
1. Connect existing data
Use vibration, temperature, current, pressure, run hours, and maintenance history—no need to start from scratch.
2. Model asset behavior
Create a digital representation of how the asset normally operates under different conditions.
3. Detect anomalies early
Spot deviations that don't trigger traditional alarms but signal emerging problems.
4. Predict failure risk
Estimate how issues will progress over time and which assets are most at risk.
5. Prioritize action
Focus maintenance where it prevents the most downtime, cost, or safety risk.
Where predictive maintenance delivers the most value
Rotating equipment
Motors, pumps, fans, compressors
- Bearing wear detection
- Imbalance and misalignment
- Lubrication issues
Production-critical machines
CNCs, presses, conveyors, packaging lines
- Performance drift and early fault detection
- Failure risk tied to production impact
Energy & utility assets
Transformers, substations, generation equipment
- Asset health monitoring
- Failure risk under load and weather conditions
Building systems
Chillers, AHUs, pumps, elevators
- Early detection of system degradation
- Reduced occupant disruption
Value across maintenance and operations roles
Reliability & maintenance engineers
- See which assets are trending toward failure
- Schedule work based on risk, not guesswork
- Reduce emergency repairs and overtime
Operations & plant managers
- Understand which issues threaten production
- Plan around maintenance with fewer surprises
- Improve schedule stability
Asset & engineering leaders
- Compare asset health across fleets and sites
- Identify chronic failure modes
- Support condition-based maintenance strategies
What teams typically achieve
Results depend on asset mix and maturity, but teams often target:
- Fewer emergency call-outs and spare-part crises
- Better maintenance planning and asset life extension
The biggest gains come from preventing repeat failures—not reacting faster.
Start with one asset class. Prove value. Scale.
1. Start
Choose a high-failure or high-impact asset (e.g., motors, pumps, chillers).
2. Prove
Validate early detection and prediction against real events.
3. Scale
Expand to additional assets, lines, or sites using the same approach.
Common questions about predictive maintenance
Prevent failures instead of reacting to them
Start with one asset that causes the most pain—and build from there.