Engineering Service
Build a physics-grounded digital twin that links live sensor data to your simulation models — enabling predictive maintenance, energy optimization, what-if scenario analysis, and virtual prototyping without relying on generic enterprise platforms. Built by engineers who understand the underlying physics, not only the software layer.
Real implementations in this service area
A physics-based digital twin integrating thermocouple sensor data with a transient thermal model — predicting temperature fields across coil cross-sections and supporting recipe decisions for energy-constrained operations.
A structural health monitoring digital twin for an automotive test rig — ingesting multi-channel accelerometer data through SigProc, computing fatigue damage indicators, and alerting on condition changes.
A simulation model calibrated to live plant sensor data — providing process condition estimates at unmeasured locations and supporting what-if analysis for process changes.
Realistic use cases we could build in this domain
A digital twin tracking vibration, temperature, and load signatures of rotating equipment — using physics-informed degradation models to predict remaining useful life and maintenance windows.
A first-principles reactor model linked to flow, temperature, and composition sensors — enabling real-time yield prediction and what-if analysis for feed composition or setpoint changes.
A thermal and energy digital twin of a commercial building — integrating HVAC sensor data with dynamic thermal models to optimize setpoints, predict comfort levels, and reduce energy cost.
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Common questions about this service area and how we approach it.
A digital twin is a real-time software model of a physical asset or process. It ingests sensor data, runs physics-based simulations, and provides actionable predictions for predictive maintenance, quality control, and energy decisions — updated continuously as the physical system evolves.
A focused digital twin for a single asset or process typically takes 8–16 weeks for an initial working version, depending on model complexity and data availability. We use staged delivery with technical checkpoints to validate the physics and sensor integration early.
Requirements depend on your process. We assess available instrumentation early in the project and design the twin around realistic data availability — including approaches for sparse or irregular sensor data.
Generic Industry 4.0 platforms provide connectivity and dashboards but lack domain-specific physics. A custom digital twin embeds your process physics directly — so predictions reflect real thermal, structural, or fluid behavior rather than statistical averages from similar assets.
Review case studies with quantified engineering and process impact across thermal, vibration, and digital twin projects.