---
license: cc-by-4.0
task_categories:
- image-classification
pretty_name: Mendeley Rotor Faults — Perception Representations (signal→VLM)
tags:
- rotor-fault-diagnosis
- vibration
- signal-to-image
- unbalance
- misalignment
- looseness
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-*
---
# Mendeley rotor faults — perception representations (visual grounding)

Part of the AI4Manufacturing FORGE corpus (Category **C**, task **T-C1**) and its **first rotor-family** dataset: the fault is in the *shaft line* — unbalance, misalignment, mechanical looseness — not in a bearing or a gear. One-second vibration windows from a belt-driven rotor bench, rendered as **perception** images, one HF **config** per representation.

**Records:** 2000 across 4 configs (500 windows each); labels {'normal': 125, 'misalignment': 125, 'unbalance': 125, 'looseness': 125}.

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

| config | records | splits |
|---|---|---|
| `spectrogram` | 500 | {'train': 400, 'test': 100} |
| `scalogram` | 500 | {'train': 400, 'test': 100} |
| `waveform` | 500 | {'train': 400, 'test': 100} |
| `reshaped` | 500 | {'train': 400, 'test': 100} |

## 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 / misalignment / unbalance / looseness |
| `reasoning` | empty — this release has no reasoning track (see *Why perception-only*) |
| `cate` / `task` | `C` / `T-C1` (signal fault classification) |
| `metadata` | JSON string: representation, features (time-domain stats), computed_verdict, computed_indication, harmonic_profile, harmonic_elevation, evidence_tier, `evidence_is_gate: false`, channel, fr_hz + fr_source, test_id, window_idx, fs, image_sha256, split |

## Splits
`train` / `test`, **test-wise** (leakage-safe): each of the 20 recorded tests is a distinct physical assembly and lives wholly on one side; the last test of each condition is held out. Windows never cross tests.

## Why perception-only (measured, not assumed)
There is deliberately **no reasoning/CoT sibling**. A label-free shaft-order detector (`forge_tools.shaft_harmonics`) was run against a same-rig healthy baseline; its per-class firing rate and median 1× elevation, measured at build time, are:

| gold class | detector fires `rotor_anomaly` | median 1× elevation | 1× elevation range |
|---|---|---|---|
| normal | 0.088 | 1.6 | 0.3 – 3.9 |
| misalignment | 0.008 | 1.4 | 0.5 – 2.5 |
| unbalance | 1.000 | 28.2 | 18.2 – 38.9 |
| looseness | 0.944 | 13.4 | 1.6 – 28.1 |

Read that table honestly: **unbalance** and **looseness** are strongly detectable, but **misalignment is indistinguishable from healthy** — its entire 1× elevation range sits *inside* the healthy range, so no threshold can separate them. That is the physics, not a bug — a misalignment signature is primarily **axial**, and this bench has four *radial* accelerometers and no axial one. Unbalance and looseness are also not separable *from each other* here (both simply lift 1×). And an *absolute* harmonic gate is useless on this rig: its belt/pulley drivetrain means the **healthy** state already carries a strong 2×/3× ladder (its healthy 2× sits ~9× above its own 1×), so 'harmonic structure present ⇒ fault' fires on **97.6% of healthy windows** (122/125 in this build).

Because a compute-then-check chain-of-thought could not honestly reach 2 of the 4 classes, the labels ship as **implanted gold** (the rig operator's documented condition) and `reasoning` stays empty. The shaft-order evidence still travels on every record as **informational metadata** (`evidence_is_gate: false`) — nothing was dropped, relabelled or filtered by it, so you can audit the claim above yourself from the published rows.

## Provenance & reproducibility
Generated **deterministically** by `forge_agent/examples/mendeley_rotor/convert.py` (`e6069b7005`) → `forge_model/MENDELEY_ROTOR/convert_mendeley_rotor.py` (`7708e1353d`); see `provenance.json`.

25 evenly-strided 1 s windows per test (consecutive seconds are highly correlated; striding avoids near-duplicate rows), channel 0 (`V_coupling`), shaft rate estimated per test with a bounded+gated search that falls back to the documented nominal and records `fr_source`. 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.
- **Misalignment is unlearnable-from-physics here** and may be hard for a model too; the evidence table above is the honest prior.
- **One bench, one speed regime** (~21.8 Hz rotor, belt-driven). Cross-rig generalization is untested.
- Radial channels only — no axial instrumentation.

## Source & license
Source: **Mechanical faults in rotating machinery dataset** (normal, unbalance, misalignment, looseness) — L. Brito, G. A. Susto, J. N. Brito, M. Duarte, Mendeley Data, [doi:10.17632/zx8pfhdtnb.1](https://doi.org/10.17632/zx8pfhdtnb.1). **License: CC BY 4.0**; this derived dataset is redistributed under the same terms with attribution to the original authors.