---
license: other
license_name: not-stated-upstream
task_categories:
- image-classification
pretty_name: MaFaulDa Bearing Faults — Perception Representations (signal→VLM)
tags:
- bearing-fault-diagnosis
- vibration
- signal-to-image
- mafaulda
- rolling-element-bearing
- negative-result
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 — bearing faults, perception representations (visual grounding)

Part of the AI4Manufacturing FORGE corpus (Category **C**, task **T-C1**). One-second vibration windows from the **seeded rolling-element bearings** of the SpectraQuest Machinery Fault Simulator at SMT/COPPE/UFRJ, rendered as **perception** images — one HF **config** per representation.

This is the **bearing** half of the MaFaulDa family; the **rotor** half ships separately as `MAFAULDA-rotor` / `MAFAULDA-rotor-perception`. Same rig, opposite conclusion: the rotor half carries a reasoning track, and this one deliberately does not. **[Why that is, measured, is the point of this card](#why-perception-only--a-measured-negative-result-not-an-omission).**

**Records:** 8960 across 4 configs (2240 windows each); labels {'normal': 98, 'ball_fault': 646, 'cage_fault': 752, 'outer_race': 744}; splits {'train': 1802, 'test': 438}; positions {'none': 98, 'overhang': 1026, 'underhang': 1116}.

## Configs
```python
from datasets import load_dataset
ds = load_dataset("AI4Manufacturing/MAFAULDA-bearing-perception", "spectrogram")
```

| config | records | splits |
|---|---|---|
| `spectrogram` | 2240 | {'train': 1802, 'test': 438} |
| `scalogram` | 2240 | {'train': 1802, 'test': 438} |
| `waveform` | 2240 | {'train': 1802, 'test': 438} |
| `reshaped` | 2240 | {'train': 1802, 'test': 438} |

## 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 bearing condition: normal / ball_fault / cage_fault / outer_race |
| `reasoning` | **empty on every record** — this release has no reasoning track (see *Why perception-only*) |
| `cate` / `task` | `C` / `T-C1` (signal fault classification) |
| `metadata` | JSON string: `source_relpath` (**the unique record key**), representation, position, channel + channel_index, fr_hz + fr_source, severity + severity_value, group_id, features, fault_freqs, line_resolvability, demod_band_hz + demod_band_kurtosis, computed_verdict, computed_dominant_line, computed_snr, evidence_tier, `evidence_is_gate: false`, bearing, file, window_idx, window_start_s, window_seconds, fs, image_sha256, split |

> `source_relpath` (e.g. `underhang/ball_fault/20g/29.1264.csv`) is the **unique** key. `file` is only the shaft rate — it is the same string for every position/type/load that was recorded at that speed, so it repeats across hundreds of rows and must not be used as an identifier.

## Splits
`train` / `test`, **severity-wise and leakage-safe**: `group_id = '<position>|<label>|<severity>'`, severities sorted **numerically** (lexically `'10g' < '6g'`, which would hold out the wrong level) and the last one held out. Windows never cross files, and no (position, label, load) assembly spans both sides.

> **What `test` actually holds out is an unseen LOAD, not an unseen defect.** On the bearing trees the sub-folder is the **added imbalance mass** (0 g / 6 g / 20 g / 35 g) — an operating condition, not a defect depth. MaFaulDa seeds exactly **one physical defect per (position, type)**, so a defect-instance-wise split is impossible on this dataset. `test` therefore measures generalization to an unseen load, and a model may still have seen the same physical defect in `train`. `position`, `severity`, `severity_value` and `fr_hz` are on every row so a downstream eval can re-carve.

`normal/` has no severity level — it is one group (`none|normal|none`) and sits wholly in `train`, the same choice CWRU and the rotor sibling make for a single healthy assembly.

## Why perception-only — a measured negative result, not an omission
There is deliberately **no reasoning/CoT sibling** for the bearing half of MaFaulDa, even though the [rotor half](https://huggingface.co/datasets/AI4Manufacturing/MAFAULDA-rotor) of the *same rig* does ship one. Three independent findings, in the order of how much they should convince you.

### 1. The detector anti-correlates with the gold (measured at build time)
The same label-free envelope chain that works on MFPT and CWRU (`bearing_faultfreq` → `spectral_kurtosis_kurtogram` → `hilbert_envelope` → `match_lines`, untuned defaults) was run on every window here. Its firing rate is **highest on the healthy class**:

| gold class | windows | fires **any** fault | `verdict == gold` | median envelope SNR | max |
|---|---|---|---|---|---|
| `normal` | 98 | 14.3% | 85.7% | 0.0 | 32.1 |
| `ball_fault` | 646 | 2.0% | 0.0% | 0.0 | 49.2 |
| `cage_fault` | 752 | 6.4% | 0.0% | 0.0 | 63.0 |
| `outer_race` | 744 | 3.1% | 1.2% | 0.0 | 46.1 |

Read it plainly: the detector cries fault on **14.3% of genuinely healthy** windows and on only **2.0% / 6.4% / 3.1%** of real ball / cage / outer-race windows — it is *anti*-correlated with the truth. `verdict == gold` is **0.0% / 0.0% / 1.2%** on the fault classes; the 85.7% on `normal` is not a success, it only means *the detector found nothing*, which is what it also does on most faults. The **median winning envelope SNR is 0.0 in every class** — against **30–180** on MFPT/CWRU, where this exact chain demonstrably works. The method is not marginal here, it is off by an order of magnitude.

The sharpest single tell: **29 windows receive an `inner_race` verdict on a dataset that contains no inner-race defect at all.** MaFaulDa seeds ball, cage and outer-race faults only, so every one of those is a measured false fire — and it is exactly the fingerprint finding 2 predicts, BPFI landing on the 5× rotor line.

### 2. The rig's bearing orders coincide with its rotor harmonics
MaFaulDa is a *rotor-fault* trainer, so every record carries a strong integer shaft-harmonic ladder (1×, 2×, 3×, …). This bearing's defect orders sit **on** that ladder:

| line | shaft order | Δ orders to nearest integer rotor harmonic | resolvable? |
|---|---|---|---|
| `BPFO` | 2.9979× | 0.0021 | **collides** |
| `BPFO-1x` | 1.9979× | 0.0021 | **collides** |
| `BPFO+1x` | 3.9979× | 0.0021 | **collides** |
| `BPFOx2` | 5.9957× | 0.0043 | **collides** |
| `BPFOx3` | 8.9936× | 0.0064 | **collides** |
| `BPFI` | 5.0021× | 0.0021 | **collides** |
| `BPFI-1x` | 4.0021× | 0.0021 | **collides** |
| `BPFI+1x` | 6.0021× | 0.0021 | **collides** |
| `BPFIx2` | 10.0043× | 0.0043 | **collides** |
| `BSF` | 1.8705× | 0.1295 | escapes |
| `BSF-FTF` | 1.4957× | 0.4957 | escapes |
| `BSF+FTF` | 2.2452× | 0.2452 | escapes |
| `FTF` | 0.3747× | 0.3747 | escapes |

**BPFO = 2.9979×** is 0.0021 orders from **3×**; **BPFI = 5.0021×** is 0.0021 orders from **5×**. At the rig's typical ~29 Hz shaft rate that is **≈0.058 Hz** of separation, against a **0.500 Hz** FFT bin on a 2 s window — **0.12 bins**, unresolvable. (This build's 1 s windows give 1.0 Hz bins, i.e. 0.058 bins: worse.) The harmonics land on integers too — 2×BPFO ≈ 6×, 3×BPFO ≈ 9× — and so do the ±1× shaft sidebands, at ≈2×/4× for BPFO and ≈4×/6× for BPFI. Only the cage/ball-spin family escapes: **FTF 0.3747×**, **BSF 1.8705×** and **BSF∓FTF 1.4957× / 2.2452×**. On top of that, `match_lines`' own ±2% tolerance is ±1.4 Hz at BPFO ≈ 70 Hz, which swallows the 3× rotor harmonic whole — so even a line a finer window could *resolve* is not *separated* by the matcher. Every published row carries `line_resolvability`, marking per line whether it is separated at that record's own shaft rate, so this is auditable record by record.

### 3. The geometry is correct — this is physics, not a missing constant
The obvious objection is that the bearing constants are simply wrong. They are not: the geometry-derived multipliers reproduce **MaFaulDa's own published fault-frequency table** to better than 0.0006 orders, and the pair is regression-tested upstream.

| line | derived from geometry | MaFaulDa's published table | abs. Δ (orders) |
|---|---|---|---|
| BPFO | 2.9979× | 2.9980× | 0.00014 |
| BPFI | 5.0021× | 5.0020× | 0.00014 |
| BSF | 1.8705× | 1.8710× | 0.00054 |
| FTF | 0.3747× | 0.3750× | 0.00027 |

(A de-risk pass before the build also swept all six accelerometer axes — underhang/overhang × axial/radial/tangential — on the lines finding 2 leaves standing: fault/healthy envelope-SNR ratios came out 0.40–1.55 with no consistent direction. There is no channel on this rig where the bearing lines lift over healthy.)

### What that means for this release
A compute-then-check chain-of-thought over these images could not honestly reach *any* of the three fault classes, so writing one would be confabulation. Therefore the labels ship as **implanted gold** — the rig operator's documented seeded condition, which is sound: the defects are physically installed and the runs are steady-state. `reasoning` is `None` on every record. **No `spectrum` representation is rendered either** — an envelope spectrum with BPFO/BPFI reference lines drawn on top of the 3×/5× rotor harmonics is precisely the image that invites the confabulation this release refuses.

**Nothing is gated and nothing is dropped.** All records built by the producer are published. The envelope evidence still travels on every row as **informational metadata** (`evidence_is_gate: false`): `computed_verdict`, `computed_snr`, `computed_dominant_line`, `evidence_tier`, `fault_freqs`, `line_resolvability`. No record was filtered, relabelled or reordered by it, and nothing downstream should treat it as a filter — `evidence_tier` is present only for comparability with the gated siblings (MFPT / CWRU / XJTU). You can therefore re-derive every claim above from the published rows yourself.

## Provenance & reproducibility
Generated **deterministically** by `forge_agent/examples/mafaulda_bearing/convert.py` (`b3c9d23345`) → `forge_model/MAFAULDA_BEARING/convert_mafaulda_bearing.py` (`d6e0dab739`); see `provenance.json`, which embeds the producer's full record under `raw_form_provenance`.

**1,120 source sequences** (5 s @ 50 kHz) → 2 fixed, non-overlapping 1 s windows per file at [1.0, 3.0] s — deterministic, evenly strided, away from both ends, and identical to the rotor sibling's sampling so the two families' windows are comparable record for record.

Each file's **shaft rate is its filename** (`fr_source='filename'` on every row). The tachometer channel is deliberately **unused**: FFT peak-picking on its pulse train returns pulse-train harmonics rather than the shaft rate, which would silently mis-place every fault line. Renderers are `forge_tools` defaults; `spectrogram` and `scalogram` stay **full band** (at 1 s / nperseg=256 the STFT bin is ~195 Hz at 50 kHz, so no zoom could resolve a fault-line ladder anyway) — all four representations are **texture** channels here, not readable spectra. Images carry no titles and no condition text in their filenames (answer-leak hygiene).

## Caveats
- **Not a reasoning dataset.** Use it for representation diversity / visual grounding, or as a documented negative control for label-free bearing evidence. The compute-then-check bearing tracks in this corpus are `MFPT-*`, `CWRU-*` and `XJTU-*`.
- **Fault position is metadata, not part of the label.** `underhang` vs `overhang` says where on the shaft the defective bearing sits; the four classes are the *condition* only. A position-aware task can be re-carved from the `position` field.
- **One channel per record — the accelerometer at the fault position** (`underhang_radial` for underhang and for healthy, `overhang_radial` for overhang; `channel` / `channel_index` on every row). The rig's other five axes exist upstream but showed no separation (see finding 3).
- **Class balance follows the source**: `normal` is by far the smallest class (49 source sequences vs ~1,071 seeded ones), and being a single assembly it is train-only.
- **Speed is swept, not fixed** (~12–62 Hz shaft rate, one speed per file). Speed is in `fr_hz`; a model can learn it from the image, so speed-stratified evaluation is advisable.
- The rig's **rotor** trees (imbalance / horizontal- and vertical-misalignment) are a different physics and a different conclusion — they are the sibling `MAFAULDA-rotor` release, which *does* ship a reasoning track.

## 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/COPPE/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.