| --- |
| 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 |
| num_bytes: 3301061.0 |
| 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. |
