--- 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.