README.md

---
dataset_info:
- config_name: reshaped
  features:
  - name: query
    dtype: string
  - name: image
    dtype: image
  - name: annot
    dtype: string
  - name: reasoning
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  - name: cate
    dtype: string
  - name: task
    dtype: string
  - name: metadata
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    num_examples: 311
  - name: test
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  download_size: 1686673
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- config_name: scalogram
  features:
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  - name: image
    dtype: image
  - name: annot
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  - name: reasoning
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  - name: task
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  - name: test
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- config_name: spectrogram
  features:
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  - name: image
    dtype: image
  - name: annot
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  - name: reasoning
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  - name: cate
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- config_name: waveform
  features:
  - name: query
    dtype: string
  - name: image
    dtype: image
  - name: annot
    dtype: string
  - name: reasoning
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  - name: cate
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  - name: task
    dtype: string
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configs:
- config_name: reshaped
  data_files:
  - split: train
    path: reshaped/train-*
  - split: test
    path: reshaped/test-*
- config_name: scalogram
  data_files:
  - split: train
    path: scalogram/train-*
  - split: test
    path: scalogram/test-*
- config_name: spectrogram
  data_files:
  - split: train
    path: spectrogram/train-*
  - split: test
    path: spectrogram/test-*
- config_name: waveform
  data_files:
  - split: train
    path: waveform/train-*
  - split: test
    path: waveform/test-*
task_categories:
- image-classification
license: other
tags:
- gear-fault-diagnosis
- gearbox
- vibration
- signal-to-image
- seu
- dds
pretty_name: SEU Gearset — Perception Representations (signal→VLM)
---
# SEU gearset — perception representations (visual grounding)

The same SEU gearset windows rendered as **perception** images — one HF **config** per representation. Unlike the `SEUG` (modulation-spectrum) repo, these are **not** for compute-then-check CoT (`reasoning` stays empty).

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

| config | records | splits |
|---|---|---|
| `spectrogram` | 391 | {'train': 311, 'test': 80} |
| `scalogram` | 391 | {'train': 311, 'test': 80} |
| `waveform` | 391 | {'train': 311, 'test': 80} |
| `reshaped` | 391 | {'train': 311, 'test': 80} |

## 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 gear condition: health / chipped / miss / root / surface |
| `reasoning` | chain-of-thought (empty here; filled in the `-annotated` sibling) |
| `cate` / `task` | `C` / `T-C1` (signal fault classification) |
| `metadata` | JSON string: representation, condition, file, window_idx, start_sample, channel, fs, fr_nominal, fr_used, fr_source, planetary, gear_lines, computed_verdict, computed_score, integer_score, family_obs, evidence_tier, image_sha256, split |

## Provenance & reproducibility
Generated **deterministically** by `forge_agent/examples/seu/convert.py` (`a990b2ef69`) → `forge_model/SEUG/convert_seug.py` (`8892ffb2db`); see `provenance.json`.

**Gold = filenames** (the files' internal Title fields are provably stale operator templates); the five gear conditions are **physically implanted** on the stage-1 sun gear of the DDS planetary gearbox [evidenced: every fault class modulates the mesh at the sun-fault order 5/6·fr] and are steady-state, so every window carries its file's condition. The gear-train constants (2-stage planetary 20/40×4/100 → 24/30×3/84, 27:1) were **derived from this dataset's own spectra** and validated against the manufacturer's published 27:1 ratio — tooth counts are not published anywhere. Confidence grades: **stage 1 high** (carrier line at exactly fr/6, sun-fault line at 5/6·fr, GMF₁ = 16.665 orders with dominant 2×/4× harmonics, valid 4-planet assembly), **stage 2 moderate** (GMF₂ = 3.111 orders at both speeds; sole assembly-valid candidate). Full chain + grades in `provenance.json` (`planetary_derivation`).

## Caveats
- **The evidence tier is BINARY.** The label-independent detector (`mesh_modulation`) attests that *a gear fault is visibly present* (sun-fault-family modulation beating integer-order modulation) — it cannot name which of the four implanted subtypes, because all four share the same modulation signature. `confirmed` = binary agreement with the gold; subtype discrimination is learnable from these signals (deep-learning literature) but not physics-nameable.
- **Conflict rule (binary):** weak records are dropped only when the detector claims a fault on a `health` record; a quiet detector on a fault record is benign non-detection (kept in perception).
- **Split is time-stratified per file** (first 80% of each recording → train, last 20% → test): the rig has ONE physical specimen per (condition, speed-load) cell, so no unit-wise split exists. Cross-specimen generalization cannot be evaluated from this dataset.
- **Two operating conditions** (20 Hz-0 V, 30 Hz-2 V motor speed-load) are both included with condition metadata.

## Source & license
Source: **SEU gearbox dataset** — Southeast University, Drivetrain Dynamics Simulator (SpectraQuest/Sumyoung DDS). Authors' research release: github.com/cathysiyu/Mechanical-datasets (no LICENSE file — cite the paper): S. Shao, S. McAleer, R. Yan, P. Baldi, *IEEE Trans. Industrial Informatics* 15(4):2446–2455, 2019 (DOI 10.1109/TII.2018.2864759). fs = 5120 Hz [evidenced: DAQ header × 2.56 convention + shaft combs at nominal in both conditions]. The release's `dataset/` folder (CWRU fan-end copies) is excluded — CWRU is published separately from its original source.