| --- |
| 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: 2799834.0 |
| num_examples: 660 |
| - name: test |
| num_bytes: 804907.0 |
| num_examples: 196 |
| download_size: 3112924 |
| dataset_size: 3604741.0 |
| - 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: 73707615.0 |
| num_examples: 660 |
| - name: test |
| num_bytes: 19408454.0 |
| num_examples: 196 |
| download_size: 92514054 |
| dataset_size: 93116069.0 |
| - 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: 51987919.0 |
| num_examples: 660 |
| - name: test |
| num_bytes: 15522216.0 |
| num_examples: 196 |
| download_size: 66907507 |
| dataset_size: 67510135.0 |
| - 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: 25883822.0 |
| num_examples: 660 |
| - name: test |
| num_bytes: 7228727.0 |
| num_examples: 196 |
| download_size: 32452056 |
| dataset_size: 33112549.0 |
| 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 |
| ```python |
| 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. |
