AI4Manufacturing/MFPT-perception

MFPT — perception representations (visual grounding)

The same MFPT bearing windows rendered as perception images — one HF config per representation (spectrogram / scalogram / waveform / reshaped). Included as representation-diversity / grounding data for the foundation model; unlike the MFPT (spectrum) repo they are not for compute-then-check CoT (reasoning stays empty) — the discriminative signal is non-verbal texture.

Configs

load_dataset("AI4Manufacturing/MFPT-perception", "spectrogram")

config	records	splits
spectrogram	78	{'train': 54, 'test': 24}
scalogram	78	{'train': 54, 'test': 24}
waveform	78	{'train': 54, 'test': 24}
reshaped	78	{'train': 54, 'test': 24}

Schema (7-field unified record)

field	meaning
query	the classification instruction (one of 30 deterministic paraphrases per representation)
image	the rendered signal image (bytes embedded)
annot	gold fault class: normal / inner_race / outer_race
reasoning	chain-of-thought (empty here; filled in the -annotated sibling)
cate / task	C / T-C1 (signal fault classification)
metadata	JSON string: representation, features, fault_freqs, computed_verdict, computed_snr, evidence_tier, published_fault_hz, load_lbs, bearing, channel, fs, fr_hz, file, window_idx, image_sha256, split

Provenance & reproducibility

Generated deterministically by forge_agent/examples/mfpt/convert.py (3245b6c799) → forge_model/MFPT/convert_mfpt.py (d74c16b01b); see provenance.json for the full record.

The MFPT test bearing's geometry (8 elements, d=0.235″, D=1.245″, 0° contact) is cross-checked against each file's own published fault frequencies: geometry-derived BPFO/BPFI agree to <0.001 Hz (BSF ~0.05 Hz rounding). FTF differs by convention (MFPT publishes the cage rate relative to the rotating inner race; forge_tools reports the standard train/cage frequency) — reported, not reconciled. Labels come from the dataset's own filenames; the shaft rate (25 Hz), sample rate and load are read from each .mat. An evidence gate (a label-independent envelope-spectrum detector) confirms every published image supports its label — on this clean, well-separated data every window is confirmed and every computed verdict matches its gold (baseline → healthy with zero false positives), so no record is dropped.

Caveats

• Small, clean source — MFPT ships 20 lab files (3 baseline, 3+7 outer-race, 7 inner-race) at a single 25 Hz shaft speed. The value is a groundable, load-varying bearing benchmark (loads 0–300 lb) that complements CWRU/XJTU/IMS, not raw record volume.
• No ball class — MFPT seeds only inner- and outer-race faults (+ healthy), so this is a 3-class task (unlike CWRU's 4).
• Split — train/test follow the dataset's own by-load-condition partition (each file wholly on one side; windows never cross files, so it is leakage-safe). It is a condition split, not an unseen-bearing split (MFPT is one bearing type).
• FTF convention — the published_fault_hz in metadata uses MFPT's cage-relative-to-inner FTF; the fault_freqs use forge_tools' standard train-frequency FTF. BPFO/BPFI (which drive the verdicts) agree in both.

Source & license

Source: Society for Machinery Failure Prevention Technology (MFPT) bearing fault data (orig. Eric Bechhoefer), obtained via MathWorks' RollingElementBearingFaultDiagnosis-Data distribution. License: CC BY-NC-SA 4.0 (Attribution — NonCommercial — ShareAlike): this derived dataset is redistributed under the same terms; non-commercial use only, with attribution to MFPT / Eric Bechhoefer and MathWorks.