---
license: mit
tags:
  - predictive-maintenance
  - cmapss
  - remaining-useful-life
  - graph-neural-networks
  - jepa
  - anomaly-detection
library_name: pytorch
---

# Diagnostic Graph PDM — Best C-MAPSS Checkpoints

Best observed checkpoints from the [Diagnostic-Graph-PDM](https://github.com/Sph3inz/Diagnostic-Graph-PDM) stack: Graph-JEPA pretrain → Energy Critic → TCN+Transformer RUL with energy fusion.

## Files

| File | Role | Selection criterion |
|------|------|-------------------|
| `checkpoints/jepa/jepa_epoch0003.pt` | Graph-JEPA encoder (FD123 + unlabeled FD004) | Epoch 3 val loss 0.0025 |
| `checkpoints/critic/vulcan_epoch0030.pt` | Energy Critic + encoder (JEPA-init) | Best saved FD1234 critic (~0.97 val F1) |
| `checkpoints/rul/rul_sequence.pt` | TCN+Transformer RUL (FD001) | Best val RMSE epoch 54 — test RMSE **12.485**, NASA **294.6** |

See `checkpoints.json` for paths and metrics.

## Usage

```powershell
pip install torch typer omegaconf rich
git clone https://github.com/Sph3inz/Diagnostic-Graph-PDM.git
cd Diagnostic-Graph-PDM
pip install -e .

hf download Sph3inxz/Diagnostic-Graph-PDM --local-dir ./hf_ckpt

# Scan / localize (critic)
python -m vulcan scan data/cmapss_fd001.graphs.jsonl `
  --checkpoint hf_ckpt/checkpoints/critic/vulcan_epoch0030.pt --format summary

# RUL eval
python eval_ckpt.py hf_ckpt/checkpoints/rul/rul_sequence.pt
```

Train critic from JEPA:

```powershell
python -m vulcan train --config configs/critic_cmapss_fd1234_jepa.yaml `
  --resume-from hf_ckpt/checkpoints/jepa/jepa_epoch0003.pt
```

## Citation

If you use these weights, link the GitHub repo: https://github.com/Sph3inz/Diagnostic-Graph-PDM