Datasets:

AI4Manufacturing
/

SEUG-perception

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
        num_examples: 311
      - name: test
        num_bytes: 381088
        num_examples: 80
    download_size: 1686673
    dataset_size: 1853534
  - 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
        num_examples: 311
      - name: test
        num_bytes: 10662755
        num_examples: 80
    download_size: 51835207
    dataset_size: 52079725
  - 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
        num_examples: 311
      - name: test
        num_bytes: 11195149
        num_examples: 80
    download_size: 54406320
    dataset_size: 54650124
  - 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
        num_examples: 311
      - name: test
        num_bytes: 3356647
        num_examples: 80
    download_size: 16211894
    dataset_size: 16487309
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

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.