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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
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