Datasets:

AI4Manufacturing
/

CWRU-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: 2799834
        num_examples: 660
      - name: test
        num_bytes: 804907
        num_examples: 196
    download_size: 3112924
    dataset_size: 3604741
  - 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: 73707615
        num_examples: 660
      - name: test
        num_bytes: 19408454
        num_examples: 196
    download_size: 92514054
    dataset_size: 93116069
  - 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: 51987919
        num_examples: 660
      - name: test
        num_bytes: 15522216
        num_examples: 196
    download_size: 66907507
    dataset_size: 67510135
  - 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: 25883822
        num_examples: 660
      - name: test
        num_bytes: 7228727
        num_examples: 196
    download_size: 32452056
    dataset_size: 33112549
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: cc-by-4.0
tags:
  - bearing-fault-diagnosis
  - vibration
  - signal-to-image
  - cwru
pretty_name: CWRU Bearing Fault — Perception Representations (signal→VLM)

CWRU — perception representations (visual grounding)

The same CWRU bearing windows rendered as perception images — one HF config per representation. A frozen VLM vision encoder carries real but modest class signal from all of these (shuffle-controlled probe): on a within-condition split it scores high, but under a strict bearing-wise split (unseen bearings) it is only slightly above chance — generalizing CWRU fault type across bearings is hard for every method, including a purpose-built 1D-CNN. So these are included as representation-diversity / grounding data for the foundation model (not as a high-accuracy CWRU classifier). Unlike the CWRU (spectrum) repo, they are not for compute-then-check CoT (reasoning stays empty) — the discriminative signal is non-verbal texture.

Configs

load_dataset("AI4Manufacturing/CWRU-perception", "spectrogram")

config	records	splits
`spectrogram`	856	{'train': 660, 'test': 196}
`scalogram`	856	{'train': 660, 'test': 196}
`waveform`	856	{'train': 660, 'test': 196}
`reshaped`	856	{'train': 660, 'test': 196}

Schema (7-field unified record)

field	meaning
`query`	the classification instruction (representation-aware)
`image`	the rendered signal image (bytes embedded)
`annot`	gold fault class: normal / inner_race / outer_race / ball
`reasoning`	chain-of-thought (empty here; filled in the `-annotated` sibling)
`cate` / `task`	`C` / `T-C1` (signal fault classification)
`metadata`	JSON string: representation, features, fault_freqs, computed_verdict, computed_snr, evidence_tier, sr_nondiagnosable, anomaly, size_in, load_hp, or_position, bearing, bearing_group, channel, rpm, fs, fr_hz, file_number, window_idx, image_sha256, split

Provenance & reproducibility

Generated deterministically by forge_agent/examples/cwru/convert.py (8f7c384bda) → forge_model/CWRU/convert_cwru.py (a88573f32f); see provenance.json for the full record.

Cleaning (all encoded in the pinned code): excluded the NTN 0.028″ files 3001–3008 (wrong bearing geometry) and the corrupt 191; flagged Smith & Randall (2015) non-diagnosable (sr_nondiagnosable) and acquisition-anomaly files; bearing-wise leakage-safe split (all loads + both sample rates of a physical bearing stay on one side). Evidence-gated release: a computed evidence_tier (from the envelope-spectrum detector vs the gold label, cross-checked against the Smith & Randall expert grades) curates the published set so every image visibly supports its label — the spectrum/reasoning repo keeps confirmed only (faithful compute-then-check CoT); the perception repo keeps confirmed+weak and drops absent.

Caveats

Ball is the hard class — Smith & Randall (2015) grade most CWRU ball faults non-diagnosable (intermittent load-zone contact + smeared BSF), so many ball windows are weak/absent. The evidence gate keeps only the diagnosable ones, so the reasoning/spectrum track has the fewest ball records (confirmed-only) while perception also admits weak ball. A property of the data, not the converter.
Class balance — after gating, normal is the minority (CWRU ships few baseline files) and outer_race the majority; per-class counts are in the header above.
Eval: use the provided bearing-wise split; the within-condition (per-load) split inflates accuracy.

Source & license

Source: Case Western Reserve University Bearing Data Center (engineering.case.edu/bearingdatacenter), openly available for research. Diagnosability grades and anomaly flags from W. A. Smith & R. B. Randall, MSSP 64–65 (2015) 100–131; leakage-safe split per J. Hendriks et al., MSSP 169 (2022) 108732.