TurboSens1

The first scenario of the TurboSens turbofan degradation benchmark — an interactive simulator dataset that pairs sensor observations with the full ten-dimensional ground-truth health state of the engine at every flight cycle, enabling a direct inverse probing protocol for any self supervised world model.

TurboSens2 (the second, harder scenario with fouling, phantom observation and region archetypes) is released as a separate dataset.

Splits

Split	Episodes	Total flights	Episode length	Notes
train	400	4,000,000	10,000 (capped)	4 archetypes, 10 events
test	40	400,000	10,000 (capped)	held out, same generator and prior
test_hard	40	400,000	10,000 (capped)	OOD: stronger degradation, modified maintenance

Schema (HDF5 columns)

Path	Shape	Description
sensors	(N, 7, 12)	Sensor stream (7 channels x 12 flight phase contexts)
observation.state	(N, 10)	Ground truth mechanical wear $\mathbf{s}_t$ (probing target)
action	(N, 1)	Maintenance action index
event_mask	(N,)	Boolean: any event fired at $t$
event_types	(N, 10)	Per event type rising edge flag
weather	(N, 1)	Ambient temperature deviation
ep_offset, ep_len	(N_ep,)	Per episode flat offsets
ep_meta/{archetype, archetype_onset, eol_triggered, success_coeff}	(N_ep,)	Episode metadata

File-level attributes

• scenario: "turbosens1"
• n_episodes, n_timesteps
• action_names: [do_nothing, fan_overhaul, hpc_overhaul, turbine_overhaul, full_overhaul, targeted_patch]
• archetype_names: [A_compressor, B_fan_booster, C_turbine, D_balanced]
• event_names: abstract effect-typed labels — [perm_step_1, trans_drift_1, trans_anomaly_1, perm_step_2, trans_fouling_1, trans_anomaly_2, perm_drift_1, perm_drift_2, sensor_pulse_1, trans_drift_2]
• sensor_names: [HPC_Tout, HP_Nmech, HPC_Tin, LPT_Tin, Fuel_flow, HPC_Pout_st, LP_Nmech]

Loading

import h5py

with h5py.File("turbosens1_train.h5", "r") as f:
    sensors = f["sensors"][:]                  # (N, 7, 12)
    state   = f["observation.state"][:]       # (N, 10)
    action  = f["action"][:]                  # (N, 1)
    ep_off  = f["ep_offset"][:]               # (N_ep,)
    ep_len  = f["ep_len"][:]                  # (N_ep,)

Or via the HuggingFace datasets library (downloads only; HDF5 must be parsed with h5py):

from huggingface_hub import hf_hub_download
import h5py

p = hf_hub_download(repo_id="<namespace>/turbosens1",
                    filename="turbosens1_train.h5", repo_type="dataset")
with h5py.File(p, "r") as f:
    ...

Inverse probing protocol

1. Pretrain any world model self supervised on the train split's sensor stream alone.
2. Freeze the encoder; train a small probe head on top of the frozen embeddings to predict $\mathbf{s}_t$.
3. Evaluate on test and test_hard. Report per-component Pearson, RMSE, $R^2$.

Reference baselines (JEPA, AR-LSTM, RSSM) and probe code live in the companion GitHub repository.

Versioning

Generated by turbosens1@v1.0.0 (deterministic simulator, stamped in SIM_VERSION).

Caveats and intended use

• Synthetic data, not a calibration of any real fleet. Stochastic event rates and magnitudes are mathematical abstractions and do not reflect operational fleet failure statistics.
• Single domain. Cross-domain generalisation claims should not be made from TurboSens alone.
• Linear probe sufficiency. The protocol assumes a linear probe is expressive enough; encoders that encode the state in a non-linearly decodable form will appear to fail at probing — informative, not definitive about representation quality.

Full RAI metadata is in croissant.json.

License

CC-BY-4.0.