Solutions
From Virtual Insight to Real-World Performance
Solution Overview
A Digital Twin is a virtual replica of a physical asset, enabling real-time monitoring, simulation, and optimization. GT-SUITE offers a comprehensive platform to develop digital twins, integrating robust multi-physics simulation with state-of-the-art data science. Powered by a cloud-based simulation environment that can seamlessly connect with the customer’s data collection system, GT’s solution helps enhance asset performance, reduce downtime and improve decision-making. Unlock the full potential of your systems by leveraging GT-SUITE to build your Digital Twin.
Application Highlights
Connected to a customer’s own data collection system, the model runs around the clock: watching machine health, flagging faults before they become failures, and giving engineers a sandbox for scenarios too costly or risky to run on real hardware.
ADVANCED FEATURES
Six capabilities turn the model into a tool your team uses daily, not a one-time simulation exercise.
GT-SUITE's digital twin continuously compares an asset's live sensor data against its calibrated baseline and flags any deviation as soon as it appears. Because the model represents how the asset's subsystems interact physically, engineers can trace a flagged deviation back through the model to its likely root cause, rather than starting the investigation from raw data alone.
Running the digital twin forward from an asset's current state projects how a developing issue is likely to progress, rather than only reporting a fault once it appears. That advance warning turns a breakdown that would otherwise stop the line into a repair scheduled well ahead of failure.
A digital twin tracks an asset's live operating data continuously and layers simulated scenarios on top of it, letting engineers test a change before committing resources to it. That gives day-to-day operating decisions a data-backed basis instead of relying on experience alone.
Field data collected from a physical asset feeds back into the same physics-based model used to design it, connecting how the product performs in operation to how the next version gets engineered. Basing design decisions on that operating data, rather than assumption, shortens development time.
Because a digital twin forecasts which components on an asset are approaching failure, procurement teams can stock replacement parts ahead of need instead of reacting to an outage. The same simulation gives operations teams a live view of system performance to plan daily work around.
GT-SUITE's digital twin reproduces an asset's real-world operating conditions closely enough to validate a design change before it reaches a physical prototype. Shifting part of that verification work from the test bench to the simulation environment cuts both cost and the time a physical test program requires.
Explore frequently asked questions about GT-SUITE digital twin capabilities.
The digital twin compares live sensor data against its calibrated baseline continuously, not just at scheduled checkpoints, which gives it enough resolution to separate a genuine deviation from normal operating noise. That balance between catching true faults and filtering out false alarms is what keeps a plant floor from reacting to every blip in the data.
Enough to turn an unplanned stoppage into a scheduled repair. Running the model forward from the asset’s current state projects how a developing issue is likely to progress, rather than only reporting a fault once it has already happened, which is the difference between reacting to downtime and planning around it.
Yes. Physical testing of every operating condition is expensive, slow, and often impossible to run safely, so the digital twin lets you simulate those conditions instead. It also gives you virtual sensors, meaning you can monitor points on a system that were never physically instrumented, and still anticipate failures at those points.
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