README.md

---
license: cc-by-nc-sa-4.0
task_categories:
- image-classification
pretty_name: MFPT Bearing Fault — Perception Representations (signal→VLM)
tags:
- bearing-fault-diagnosis
- vibration
- signal-to-image
- mfpt
configs:
- config_name: spectrogram
  data_files:
  - split: train
    path: spectrogram/train-*
  - split: test
    path: spectrogram/test-*
- config_name: scalogram
  data_files:
  - split: train
    path: scalogram/train-*
  - split: test
    path: scalogram/test-*
- config_name: waveform
  data_files:
  - split: train
    path: waveform/train-*
  - split: test
    path: waveform/test-*
- config_name: reshaped
  data_files:
  - split: train
    path: reshaped/train-*
  - split: test
    path: reshaped/test-*
---
# 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
```python
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.