| --- |
| 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: 3302553.0 |
| num_examples: 709 |
| - name: test |
| num_bytes: 1154688.0 |
| num_examples: 242 |
| download_size: 3957400 |
| dataset_size: 4457241.0 |
| - 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: 95925624.0 |
| num_examples: 709 |
| - name: test |
| num_bytes: 33836427.0 |
| num_examples: 242 |
| download_size: 129130854 |
| dataset_size: 129762051.0 |
| - 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: 119552749.0 |
| num_examples: 709 |
| - name: test |
| num_bytes: 41315823.0 |
| num_examples: 242 |
| download_size: 160237523 |
| dataset_size: 160868572.0 |
| - 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: 28487150.0 |
| num_examples: 709 |
| - name: test |
| num_bytes: 9645131.0 |
| num_examples: 242 |
| download_size: 37435612 |
| dataset_size: 38132281.0 |
| 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-* |
| tags: |
| - bearing-fault-diagnosis |
| - vibration |
| - signal-to-image |
| - xjtu-sy |
| - run-to-failure |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| pretty_name: XJTU-SY Bearing Run-to-Failure — Perception Representations (signal→VLM) |
| --- |
| # XJTU-SY — perception representations (visual grounding) |
|
|
| The same XJTU-SY run-to-failure snapshots rendered as **perception** images — one HF **config** per representation, for the foundation model's visual grounding. Unlike the `XJTU` (spectrum) repo, these are **not** for compute-then-check CoT (`reasoning` stays empty). |
|
|
| ## Configs |
| ```python |
| load_dataset("AI4Manufacturing/XJTU-perception", "spectrogram") |
| ``` |
|
|
| | config | records | splits | |
| |---|---|---| |
| | `spectrogram` | 951 | {'train': 709, 'test': 242} | |
| | `scalogram` | 951 | {'train': 709, 'test': 242} | |
| | `waveform` | 951 | {'train': 709, 'test': 242} | |
| | `reshaped` | 951 | {'train': 709, 'test': 242} | |
|
|
| ## Schema (7-field unified record) |
| | field | meaning | |
| |---|---| |
| | `query` | the classification instruction (one of 30 deterministic paraphrases per representation) | |
| | `image` | the rendered signal image (bytes embedded) | |
| | `annot` | gold fault class: normal / inner_race / outer_race | |
| | `reasoning` | chain-of-thought (empty here; filled in the `-annotated` sibling) | |
| | `cate` / `task` | `C` / `T-C1` (signal fault classification) | |
| | `metadata` | JSON string: representation, condition, bearing_id, file_number, time_frac, life_files, channel, bearing, rpm, fs, fr_nominal, fr_used, fr_source, features, fault_freqs, computed_verdict, computed_snr, evidence_tier, image_sha256, split | |
|
|
| ## Provenance & reproducibility |
| Generated **deterministically** by `forge_agent/examples/xjtu/convert.py` (`250c7e5f89`) → `forge_model/XJTU/convert_xjtu.py` (`229ee98152`); see `provenance.json`. |
|
|
| **Gold = the documented teardown failure element** (Table 3 of the dataset paper): outer_race = bearings 1_1/1_2/1_3/2_2/2_4/2_5/3_1/3_5, inner_race = 2_1/3_3/3_4. `normal` = early files (before min(30% of life, the data-driven degradation onset)); bearings whose onset is floor-bound (degrading from day one) contribute no normals. Each record was scored under both the nominal and a spectrum-refined shaft rate (rigs deviate 0.5–2% from nominal); the better envelope-pattern match won. |
| |
| ## Caveats |
| - **Evidence-gated, conflict-free release.** The reasoning track keeps only `confirmed` records (the label-independent envelope-spectrum detector independently finds the documented fault). The perception tracks keep `confirmed` + non-conflicting `weak`; records where the detector confidently found a **different** pattern than the gold (e.g. bearing 2_5's healthy shaft harmonic aliasing into BPFI within 1.7%) are dropped — an image should never fight its own label. |
| - **`cage` is EXCLUDED from this release.** XJTU has two cage-failure bearings, but only 6/142 files confirm a cage (FTF-ladder) signature — and 57 fault files score as outer_race instead (a failing cage hammers the outer raceway; 8×FTF ≡ BPFO for this geometry). Retained in the raw form; the cage bearings' certified-healthy early files still serve as `normal`. Published classes: **normal / inner_race / outer_race**. |
| - **TRUE bearing-wise split** — the first run-to-failure set with enough bearings for it: test = whole held-out bearings (1_3, 2_5 outer; 3_3 inner; plus cage bearing 2_3, which after the cage exclusion contributes early-life `normal` records only), so evaluation is on unseen bearings; `normal` appears in both splits from disjoint bearings. Compound-failure bearings (1_5, 3_2) appear only as early-life `normal`. |
| - **End-of-life masking** — in the final ~3% of life, broadband breakdown can mask the discrete fault comb, so late windows are not uniformly `confirmed`. A property of the physics, not the converter. |
| |
| ## Source & license |
| Source: **XJTU-SY bearing datasets** — Xi'an Jiaotong University & Changxing Sumyoung Technology; 15 LDK UER204 bearings run to failure under 3 conditions (2100/2250/2400 rpm, 12/11/10 kN); horizontal-channel accelerometer snapshots (25.6 kHz, 1.28 s per minute). Cite: B. Wang, Y. Lei, N. Li, N. Li, *IEEE Trans. Reliability* 69(1):401–412, 2020 (DOI 10.1109/TR.2018.2882682). Gold labels: Table 3 of Lei et al., *J. Mech. Eng.* 55(16), 2019 (DOI 10.3901/JME.2019.16.001). Released by the authors for research use (biaowang.tech/xjtu-sy-bearing-datasets). |
