---
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: 29289187.0
    num_examples: 492
  - name: test
    num_bytes: 8886960.0
    num_examples: 147
  download_size: 37617733
  dataset_size: 38176147.0
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.