Digital Twin
& IoT.
A physics-based digital twin is constructed during the design phase and deployed with every product. Real-time field telemetry feeds back into the AI platform continuously — every unit in the field makes the next design better. Python, InfluxDB, Grafana, and MQTT. No proprietary platforms.
The twin is built
during the design — not after.
Most digital twins are retrofitted to existing products — sensors bolted on, data collected, models approximated from external measurements. The Power Flux AI digital twin starts at the design phase. The physics models used to design the product become the digital twin deployed with it.
The thermal FEA model that predicted junction temperature becomes the real-time thermal monitor. The control model that validated the AI-FOC algorithm becomes the performance anomaly detector. The electromagnetic model that optimized the motor becomes the bearing fault predictor. No approximation. No calibration. The model is the product.
"Every unit in the field is a data point feeding back into the AI platform. After 1,000 units, the platform knows things about long-term performance, failure modes, and operating patterns that no incumbent can match — because incumbents are not collecting this data systematically."
From design model
to field intelligence.
Real-Time Junction Temperature
Continuous estimation of SiC MOSFET and IGBT junction temperatures using the physics-based thermal network model. No additional temperature sensors required. Alerts on thermal margin violation before component stress occurs.
Bearing & Winding Fault Detection
Motor current signature analysis (MCSA) for bearing fault detection, rotor bar fault detection, and winding insulation degradation monitoring. Neural network classifier trained on fault signatures from the design FEA model. Predictive maintenance before catastrophic failure.
THDi & Harmonic Trending
Continuous measurement and trending of THDi, individual harmonic orders, power factor, and efficiency. Alerts on harmonic compliance threshold violation. Correlates power quality with load profile and ambient conditions to detect drive degradation.
System Efficiency Mapping
Real-time measurement of motor-drive system efficiency across the operating range. Efficiency map updated continuously from field data. Deviation from design efficiency curve triggers investigation alert. Compressor COP monitoring for HVAC applications.
AI-FOC Parameter Adaptation
AI-FOC control parameters adapted in real time based on motor parameter estimation from field measurements. Compensates for winding resistance drift with temperature, flux linkage change with aging, and load inertia variation. The drive gets better over time.
Design Improvement Loop
Aggregated field data from all deployed units feeds back into the AI design platform. Long-term failure modes inform design rule updates. Operating pattern analysis updates thermal derating curves. Each generation of product is better than the last because of what the previous generation learned.
Open source.
Production grade.
Deploy a digital twin with your next product.
Every Power Flux AI design engagement includes digital twin construction. Contact us to discuss deploying field intelligence on your existing or next-generation product.