# Bluecoderhub

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Shankar R](https://cerebralvalley.ai/u/Shankar_1503)
- **GitHub:** https://github.com/shankarr1503/Meta_Hackthon
- **Demo video:** https://drive.google.com/file/d/1D-miN3IMaKlLVs0dAjLwJv3lyWQdPalV/view?usp=sharing
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1889

FailureLab AI

      An AI investigator that finds out why a machine vibrates by running experiments. It says what it expects to see before every result, and it answers your questions from the evidence.

      Problem
      When a rotating machine starts to vibrate, the alarm names a symptom, not a cause. At a single operating point, imbalance, misalignment and a failing bearing can look alike, so an engineer has to decide which speed and load to test next. Every test costs time and machine downtime, and the reasoning behind a diagnosis is rarely written down. Siemens' True Cost of Downtime 2024 estimates that unplanned downtime costs the world's 500 biggest companies $1.4 trillion a year.

      What it does
      FailureLab AI hands the investigation to a Meta model (Muse Spark 1.3) and makes every step inspectable:

      1. Starting beliefs from the maintenance note. Every possible fault keeps at least 5%, so a wrong or malicious note can't rule out the true cause.
      2. Experiment design. A Bayesian engine ranks experiments by expected information per unit of cost. The model picks one of the top five and states the reading it expects under each hypothesis before the experiment runs.
      3. Physics-based digital twin. A simulated rotor on bearings produces every measurement. The engine updates the probabilities, and the dashboard shows each prediction next to the actual reading.
      4. Diagnosis and action plan. At 90% confidence, or when the budget runs out, the model explains the diagnosis, the remaining doubts and the next check. The dashboard adds a maintenance plan for that fault class.
      5. Questions for the investigator. You can ask "Why not imbalance?" and the model answers from the recorded evidence. Its answers can never change a number.
      6. Unseen machines. Any seed generates a new hidden fault that neither the developers nor the model know in advance, so judges can investigate one live.

## More from Meta Global AI Developer Hackathon

- [Dhanya808](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1886)
- [AI Innovators](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1887)
- [SpriteForge](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1888)
- [MultAI](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1890)
- [Ad Astra Simul](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1891)
- [Apex](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1892)

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