Datasets:

AI4Manufacturing
/

MAFAULDA-rotor-perception

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

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)

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.
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.
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.