Datasets:

AI4Manufacturing
/

SEUB-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: 1458123
        num_examples: 318
      - name: test
        num_bytes: 367760
        num_examples: 80
    download_size: 1659740
    dataset_size: 1825883
  - 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
        num_examples: 318
      - name: test
        num_bytes: 10747608
        num_examples: 80
    download_size: 52918492
    dataset_size: 53156580
  - 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
        num_examples: 318
      - name: test
        num_bytes: 11112062
        num_examples: 80
    download_size: 54956647
    dataset_size: 55195016
  - 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
        num_examples: 318
      - name: test
        num_bytes: 3301061
        num_examples: 80
    download_size: 16186215
    dataset_size: 16448372
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

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.