README.md

metadata

dataset_info:
  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: 29289187
      num_examples: 492
    - name: test
      num_bytes: 8886960
      num_examples: 147
  download_size: 37617733
  dataset_size: 38176147
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
task_categories:
  - image-classification
license: cc-by-4.0
tags:
  - bearing-fault-diagnosis
  - vibration
  - signal-to-image
  - cwru
  - compute-then-check
pretty_name: CWRU Bearing Fault — Envelope Spectrum (signal→VLM, Category C)

CWRU — bearing fault classification from the envelope spectrum (reasoning track)

Part of the AI4Manufacturing FORGE corpus (Category C, task T-C1). Each record is the envelope spectrum of a 1 s rolling-element-bearing vibration window with the fault frequencies marked — the representation that supports faithful compute-then-check chain-of-thought. reasoning is empty here; the CWRU-annotated sibling fills it.

Records: 639 (splits {'train': 492, 'test': 147}); labels {'inner_race': 176, 'ball': 39, 'outer_race': 389, 'normal': 35}; evidence_tier {'confirmed': 639}.

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

Splits

train / test, bearing-wise (leakage-safe): all loads + both sample rates of a physical bearing stay on one side. normal is a single healthy bearing, so it is entirely in train and the test split is fault-only (inner/outer/ball from unseen bearings).

Provenance & reproducibility

Generated deterministically by forge_agent/examples/cwru/convert.py (8f7c384bda) → forge_model/CWRU/convert_cwru.py (399478ff45); 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.