| 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 | |