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README.md
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path: waveform/train-*
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- split: test
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path: waveform/test-*
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---
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path: waveform/train-*
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- split: test
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path: waveform/test-*
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tags:
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- bearing-fault-diagnosis
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- vibration
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- signal-to-image
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- xjtu-sy
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- run-to-failure
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license: cc-by-4.0
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task_categories:
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- image-classification
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pretty_name: XJTU-SY Bearing Run-to-Failure — Perception Representations (signal→VLM)
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---
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# XJTU-SY — perception representations (visual grounding)
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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).
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## Configs
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```python
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load_dataset("AI4Manufacturing/XJTU-perception", "spectrogram")
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```
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| config | records | splits |
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|---|---|---|
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| `spectrogram` | 951 | {'train': 709, 'test': 242} |
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| `scalogram` | 951 | {'train': 709, 'test': 242} |
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| `waveform` | 951 | {'train': 709, 'test': 242} |
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| `reshaped` | 951 | {'train': 709, 'test': 242} |
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## Schema (7-field unified record)
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| field | meaning |
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|---|---|
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| `query` | the classification instruction (one of 30 deterministic paraphrases per representation) |
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| `image` | the rendered signal image (bytes embedded) |
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| `annot` | gold fault class: normal / inner_race / outer_race |
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| `reasoning` | chain-of-thought (empty here; filled in the `-annotated` sibling) |
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| `cate` / `task` | `C` / `T-C1` (signal fault classification) |
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| `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 |
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## Provenance & reproducibility
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Generated **deterministically** by `forge_agent/examples/xjtu/convert.py` (`250c7e5f89`) → `forge_model/XJTU/convert_xjtu.py` (`229ee98152`); see `provenance.json`.
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**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.
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## Caveats
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- **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.
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- **`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**.
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- **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`.
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- **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.
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## Source & license
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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).
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