--- 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: 1472446.0 num_examples: 311 - name: test num_bytes: 381088.0 num_examples: 80 download_size: 1686673 dataset_size: 1853534.0 - 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: 41416970.0 num_examples: 311 - name: test num_bytes: 10662755.0 num_examples: 80 download_size: 51835207 dataset_size: 52079725.0 - 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: 43454975.0 num_examples: 311 - name: test num_bytes: 11195149.0 num_examples: 80 download_size: 54406320 dataset_size: 54650124.0 - 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: 13130662.0 num_examples: 311 - name: test num_bytes: 3356647.0 num_examples: 80 download_size: 16211894 dataset_size: 16487309.0 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: - 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.