Datasets:
File size: 6,822 Bytes
6152003 b0ccb20 aab6df6 6152003 05eb883 6152003 b0ccb20 aab6df6 6152003 05eb883 c2a5fe6 6152003 c2a5fe6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | ---
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). |