Picks one of 10 held-out engine sequences from NASA's simulated FD001 test set. The official answer appears only after the model commits.
Same columns as the sample file. download the sample CSV
Run log
Sensor traces
Estimates how many cycles a turbofan engine has left, from its sensor history. Built by Alvin Alias.
Picks one of 10 held-out engine sequences from NASA's simulated FD001 test set. The official answer appears only after the model commits.
Same columns as the sample file. download the sample CSV
| 15.85 | this model, local evaluation |
| 17.47 | Ridge baseline, valid direct local comparison |
| 16.14 | Zheng 2017 reported value, context only |
| 18.45 | Babu 2016 reported value, context only |
100 engines run to failure · 20,631 training cycles · 14 informative sensors after dropping 7 flat channels
The literature protocols have not been audited for parity with this repository. Only the local Ridge result is a direct comparison; the published values are context, not evidence that this model beat them.
I split by engine, never at random. A random split drops cycles from the same engine into both train and test, so the model learns that engine's own drift instead of the shape of degradation.
The features are 30-cycle rolling means and spreads. Thirty cycles is a chosen smoothing horizon, not a measured fouling period or a validated optimum.
The largest SHAP attribution is associated with the rolling mean of HPC outlet temperature. That is consistent with a degradation interpretation, but SHAP describes this model's associations and does not establish physical cause.