# PRAETOR

- **Event:** [RAISE Summit Hackathon](https://cerebralvalley.ai/e/raise-summit-hackathon)
- **When:** Jul 4 at 9:00 AM – Jul 5 at 7:00 PM (GMT+2)
- **Where:** Paris, France
- **Team:** [Saad EL BABIDI](https://cerebralvalley.ai/u/Saadzw), [Alberto Gil](https://cerebralvalley.ai/u/Alberto_Gil), [Charlotte Crocicchia](https://cerebralvalley.ai/u/CharlotteC), [Tim Vignon](https://cerebralvalley.ai/u/Frisatim), [manjunath bhaskar](https://cerebralvalley.ai/u/Mkalki)
- **GitHub:** https://github.com/Saadzwak/Crusoe/tree/feat/ui-legion
- **Demo video:** https://youtu.be/WsKtNgepqvE
- **Gallery:** https://cerebralvalley.ai/e/raise-summit-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/raise-summit-hackathon/hackathon/gallery/69

PREATOR a physics-grounded digital twin for factory predictive maintenance
PREATOR is a live digital twin of a factory floor that predicts machine failures without adding any new sensors using only the sensors already present, combined with physics.
The system has three layers, modeled on a real factory's chain of command. At the base, each machine is represented by a Multi-Head Physics-Informed Neural Network (MH-PINN) that embeds governing equations from bearing kinematics (BPFO/BPFI/BSF), cure chemistry (Arrhenius, Kamal-Sourour), and fatigue mechanics (Hertzian contact, Basquin, Miner) directly into the model. Because the physics constrains the solution space, the model can reconstruct physical quantities at points that were never instrumented — we demonstrate this by withholding a real sensor entirely and reconstructing its signal with 13% accuracy on real bearing data, consistent with published PINN research. The middle layer is a line-level LLM that reads each machine's physical state, reasons over it, and explains it to a non-technical operator in plain language, always citing the actual data it consulted. The top layer is a central LLM that aggregates every machine's state through a shared store to hold the plant-wide picture.
The operator stays in control: they can question any recommendation and override it in the moment. Every override feeds a Champion/Challenger learning loop — a live model serves predictions while a challenger retrains in the background on operator corrections, then takes over when it outperforms — so both the reasoning layer and the physical model sharpen with every human decision.
The use case is tire manufacturing, focused on the curing press, using public datasets (NASA C-MAPSS, CWRU and NASA IMS bearings, AI4I 2020) as physically-plausible stand-ins no confidential manufacturer data is used. The LLM layers run on Nvidia's Nemotron Ultra, served through Crusoe Managed Inference for real-time latency across all three layers.

## More from RAISE Summit Hackathon

- [MANTRA](https://cerebralvalley.ai/e/raise-summit-hackathon/hackathon/gallery/66)
- [low cortisol - FlowTwin](https://cerebralvalley.ai/e/raise-summit-hackathon/hackathon/gallery/67)
- [AI Collective Kansas City](https://cerebralvalley.ai/e/raise-summit-hackathon/hackathon/gallery/68)
- [RAISEMYKNOWLEDGE](https://cerebralvalley.ai/e/raise-summit-hackathon/hackathon/gallery/70)
- [Overturn](https://cerebralvalley.ai/e/raise-summit-hackathon/hackathon/gallery/71)
- [Hello](https://cerebralvalley.ai/e/raise-summit-hackathon/hackathon/gallery/72)

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Markdown version of https://cerebralvalley.ai/e/raise-summit-hackathon/hackathon/gallery/69. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt