README.md

---
dataset_info:
- config_name: reshaped
  features:
  - name: query
    dtype: string
  - name: image
    dtype: image
  - name: annot
    dtype: string
  - name: reasoning
    dtype: 'null'
  - name: cate
    dtype: string
  - name: task
    dtype: string
  - name: metadata
    dtype: string
  splits:
  - name: train
    num_bytes: 1458123.0
    num_examples: 318
  - name: test
    num_bytes: 367760.0
    num_examples: 80
  download_size: 1659740
  dataset_size: 1825883.0
- config_name: scalogram
  features:
  - name: query
    dtype: string
  - name: image
    dtype: image
  - name: annot
    dtype: string
  - name: reasoning
    dtype: 'null'
  - name: cate
    dtype: string
  - name: task
    dtype: string
  - name: metadata
    dtype: string
  splits:
  - name: train
    num_bytes: 42408972.0
    num_examples: 318
  - name: test
    num_bytes: 10747608.0
    num_examples: 80
  download_size: 52918492
  dataset_size: 53156580.0
- config_name: spectrogram
  features:
  - name: query
    dtype: string
  - name: image
    dtype: image
  - name: annot
    dtype: string
  - name: reasoning
    dtype: 'null'
  - name: cate
    dtype: string
  - name: task
    dtype: string
  - name: metadata
    dtype: string
  splits:
  - name: train
    num_bytes: 44082954.0
    num_examples: 318
  - name: test
    num_bytes: 11112062.0
    num_examples: 80
  download_size: 54956647
  dataset_size: 55195016.0
- config_name: waveform
  features:
  - name: query
    dtype: string
  - name: image
    dtype: image
  - name: annot
    dtype: string
  - name: reasoning
    dtype: 'null'
  - name: cate
    dtype: string
  - name: task
    dtype: string
  - name: metadata
    dtype: string
  splits:
  - name: train
    num_bytes: 13147311.0
    num_examples: 318
  - name: test
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    num_examples: 80
  download_size: 16186215
  dataset_size: 16448372.0
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:
- bearing-fault-diagnosis
- gearbox
- vibration
- signal-to-image
- seu
- dds
pretty_name: SEU Bearingset — Perception Representations (signal→VLM)
---
# SEU bearingset — perception representations (visual grounding)

The SEU bearingset windows rendered as **perception** images — one HF **config** per representation, for the foundation model's visual grounding (subtype discrimination here is texture-learnable, not physics-nameable — see caveats).

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

| config | records | splits |
|---|---|---|
| `spectrogram` | 398 | {'train': 318, 'test': 80} |
| `scalogram` | 398 | {'train': 318, 'test': 80} |
| `waveform` | 398 | {'train': 318, 'test': 80} |
| `reshaped` | 398 | {'train': 318, '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 bearing condition: health / inner_race / outer_race / ball / inner_outer_comb |
| `reasoning` | chain-of-thought (empty here; a `-annotated` sibling may fill it later) |
| `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, family_order, family_hz, evidence_tier, image_sha256, split |

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

**Gold = filenames** (the files' internal Title fields are provably stale — `ball_30_2` is titled "outer_30_2" yet its data is demonstrably distinct); the five bearing conditions are **physically implanted** in the DDS parallel gearbox (kit menu matches the class set; `comb` = documented inner+outer combination) and steady-state. The bearing model/geometry is **unpublished**, so the evidence detector names no BPFO/BPFI: it reports the strongest modulation family not attributable to the known gear train (whose constants were derived from this dataset's own spectra; stage-1 grades high, stage-2 moderate — full chain in `provenance.json` `planetary_derivation`).

## Caveats
- **No named bearing physics.** The parallel-gearbox bearing model is unpublished; the label-independent detector (`modulation_family`) attests that *abnormal modulation is present at orders not attributable to the excluded STAGE-1 gear-train set* (carrier half-harmonics, planet spin, shaft integers). Stage-2 planetary orders are deliberately NOT excluded (excluding them would blind genuine sub-carrier bearing modulation) — so a confirmed family can in principle sit on an unmodelled stage-2 line; the tier is a **binary amplitude-anomaly attestation**, not a named-fault identification. Its operating point favours a clean healthy class (measured health false-alarm 0% at prominence 20); the cost falls on the FAULT side — at this threshold no fault class reaches `confirmed` (see provenance `detector_operating_point` for the measured per-class fractions), which is exactly why this release is perception-only. 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 (none occurred at this operating point); a quiet detector on a fault record is benign non-detection (kept in perception).
- **Split is time-stratified per file** (first 80% → train, last 20% → test): ONE physical specimen per (condition, speed-load) cell — 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) included with condition metadata. Two absent-tier windows (no modulation energy at all) are dropped, hence 398 records per config rather than 400.

## 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]. The release's `dataset/` folder (CWRU fan-end copies) is excluded.