Datasets:

AI4Manufacturing
/

XJTU-perception

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
        num_examples: 709
      - name: test
        num_bytes: 1154688
        num_examples: 242
    download_size: 3957400
    dataset_size: 4457241
  - 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
        num_examples: 709
      - name: test
        num_bytes: 33836427
        num_examples: 242
    download_size: 129130854
    dataset_size: 129762051
  - 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
        num_examples: 709
      - name: test
        num_bytes: 41315823
        num_examples: 242
    download_size: 160237523
    dataset_size: 160868572
  - 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
        num_examples: 709
      - name: test
        num_bytes: 9645131
        num_examples: 242
    download_size: 37435612
    dataset_size: 38132281
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

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).