README.md

---
license: other
license_name: not-stated-upstream
task_categories:
- image-classification
pretty_name: MaFaulDa Rotor Faults — Perception Representations (signal→VLM)
tags:
- rotor-fault-diagnosis
- vibration
- signal-to-image
- mafaulda
- unbalance
- misalignment
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-*
---
# MaFaulDa — rotor faults, perception representations (visual grounding)

The same MaFaulDa rotor windows rendered as **perception** images — one HF **config** per representation. Included for representation diversity / visual grounding; the compute-then-check track is the sibling **`MAFAULDA-rotor`** repo.

## Configs
```python
load_dataset("AI4Manufacturing/MAFAULDA-rotor-perception", "spectrogram")
```

| config | records | splits |
|---|---|---|
| `spectrogram` | 1760 | {'train': 1472, 'test': 288} |
| `scalogram` | 1760 | {'train': 1472, 'test': 288} |
| `waveform` | 1760 | {'train': 1472, 'test': 288} |
| `reshaped` | 1760 | {'train': 1472, 'test': 288} |

## 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 rotor condition: normal / imbalance / horizontal_misalignment / vertical_misalignment |
| `reasoning` | chain-of-thought (empty here; filled in the `-annotated` sibling) |
| `cate` / `task` | `C` / `T-C1` (signal fault classification) |
| `metadata` | JSON string: representation, features, harmonic_profile, harmonic_elevation, computed_verdict, family_indication, subtype_is_implanted_gold, evidence_tier, baseline_files, channel, fr_hz, severity, group_id, file, window_idx, fs, image_sha256, split |

## Splits
`train` / `test`, **severity-wise** (leakage-safe): one severity level is one physical assembly and lives wholly on one side; the highest severity of each fault class is held out. Windows never cross files.

## What the evidence supports — three levels, all measured
This is the corpus's **first rotor dataset with a reasoning track**, and the honest scope of that evidence is narrower than the label set. Measured at build time on the overhang **tangential** channel against a **speed-matched healthy baseline**:

| gold class | fires `rotor_anomaly` | 1× elev (med) | 2× | 3× | family indication when fired |
|---|---|---|---|---|---|
| normal | 0.184 | 0.98 | 1.02 | 0.95 | misalignment 78% / unbalance 22% (n=18) |
| imbalance | 0.737 | 2.76 | 2.50 | 1.01 | misalignment 57% / unbalance 43% (n=491) |
| horizontal_misalignment | 0.612 | 1.27 | 1.79 | 1.69 | misalignment 87% / unbalance 13% (n=241) |
| vertical_misalignment | 0.741 | 1.13 | 2.85 | 1.18 | misalignment 91% / unbalance 9% (n=446) |

1. **Binary anomaly (gated).** "Is this window anomalous versus the same rig's own healthy baseline?" This is what `evidence_tier` gates on; the reasoning repo keeps `confirmed` only.
2. **Family indication (not a gate, and directional).** A fired window that *looks misalignment-like*. Misalignment files read `misalignment` ~87–91% of the time — but imbalance files split ~57/43, because added mass lifts 2× on this rig too. **It is not an imbalance detector**, and a chain-of-thought written over these records must not claim it is.
3. **Subtype is implanted gold.** Horizontal vs vertical misalignment is **not** separable by this evidence; those records carry `subtype_is_implanted_gold: true`. The distinction comes from the rig operator's documented setup, not from the signal.

**Healthy false-positive rate ≈18%** — structural, not a defect: MaFaulDa sweeps 12–61 Hz and every file runs at a different speed, so a healthy window is scored against neighbours ~1 Hz away and speed-tracking resonances read as elevation. Those windows tier `weak` and drop from the reasoning track. The threshold was deliberately **not** tuned against the gold labels.

## Provenance & reproducibility
Generated **deterministically** by `forge_agent/examples/mafaulda_rotor/convert.py` (`b3c9d23345`) → `forge_model/MAFAULDA_ROTOR/convert_mafaulda_rotor.py` (`d6e0dab739`); see `provenance.json`.

Each file's **shaft rate is its filename** (re-verified at build: median healthy raw 1× SNR 17.4); the tachometer channel is deliberately unused because FFT peak-picking on it returns different pulse-train harmonics. Baselines are **speed-matched**: the three nearest-speed healthy files, each profiled at **its own** shaft rate then combined by median — profiling them at the target's rate under-measures the baseline ~30% and inflates every elevation. The three files actually used are recorded on every row (`baseline_files`), so the protocol is auditable per record.

## Caveats
- **Bearing faults are excluded.** The same rig also ships 1,071 seeded *bearing* sequences (underhang/overhang). They are a different physics (impact modulation, not shaft-synchronous) and are handled separately — on this rig their characteristic orders (BPFO ≈ 3×, BPFI ≈ 5×) collide with the rotor's own harmonics, so they cannot support this kind of evidence.
- **One rig, one channel.** All records are the overhang tangential accelerometer. The axial channel showed no separation here and the radial channel none at all — measure before assuming a channel carries misalignment.
- **Class balance** follows the source (imbalance and vertical misalignment dominate; `normal` is the smallest class and, being the evidence baseline, sits wholly in `train`).
- **Split is severity-wise**: one severity level = one physical assembly. `test` holds out the highest severity of each fault class, so it measures generalization to an unseen severity.

## Source & licence
Source: **MAFAULDA — Machinery Fault Database**, Signal, Multimedia and Telecommunications Lab (SMT), COPPE/Poli, Universidade Federal do Rio de Janeiro (<http://www02.smt.ufrj.br/~offshore/mfs/>); contact Felipe M. L. Ribeiro.

> ⚠️ **Licence not stated upstream.** As of 2026-07-28 the source page carries no licence, copyright notice, terms of use or citation requirement. This derived dataset is therefore distributed **gated (manual approval)** for research use, with attribution to SMT/UFRJ. **Clear the upstream licence with the maintainers before any onward redistribution or commercial use.** If you are the rights holder and want this changed, please open a discussion on this repo.