SWAPPDigital Twin
Forge
The equipment inside the site — and the sensors that feed the models.
Monitoring
Vitals
Catch drift before it becomes downtime.
The problem
A major rotating-equipment failure means an unplanned, long and costly outage, and threshold alarms miss the slow drift that precedes the damage.
The solution
Learns each unit’s healthy profile and scores live deviation on a 0–100 scale; pauses scoring during start-up and ramp to reduce false-alarm risk.
From the interface
Representative view; data and site names are examples.
REPRESENTATIVE INTERFACE · SYNTHETIC DATA
This mockup illustrates the product workflow; it is not connected to a live system.
How it works
- Select healthy history and review the effect of each preprocessing step.
- Learn normal operating profiles using OPTICS density-based clustering.
- Validate, version, deploy or retire the model.
- Score new data against the profile, pausing during start-up and ramp.
- Show deviation on a three-dimensional equipment view.
Capabilities
- Asset tree
- Reusable preprocessing recipes
- Preprocessing preview with dropped-row and outlier summaries
- Model validation, deployment and retirement
- Deviation score on a 0–100 scale
- Three-dimensional twin view
- Training from the equipment registry
Related modules
Analytics
Vibration
Hear a fault before it's a failure.