Datasets:

AI4Manufacturing
/

IMS-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: 2285322
        num_examples: 493
      - name: test
        num_bytes: 641331
        num_examples: 138
    download_size: 2644093
    dataset_size: 2926653
  - 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: 70619574
        num_examples: 493
      - name: test
        num_bytes: 19758029
        num_examples: 138
    download_size: 89984392
    dataset_size: 90377603
  - 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: 59380301
        num_examples: 493
      - name: test
        num_bytes: 16616857
        num_examples: 138
    download_size: 75605586
    dataset_size: 75997158
  - 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: 20573590
        num_examples: 493
      - name: test
        num_bytes: 5756230
        num_examples: 138
    download_size: 25890273
    dataset_size: 26329820
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-*
pretty_name: IMS/NASA-Bearing — Perception Representations (signal→VLM)
tags:
  - bearing-fault-diagnosis
  - vibration
  - signal-to-image
  - ims
  - nasa
  - run-to-failure
license: cc-by-4.0
task_categories:
  - image-classification

IMS / NASA-Bearing — perception representations (visual grounding)

The same IMS run-to-failure windows rendered as perception images — one HF config per representation, for the foundation model's visual grounding. Unlike the IMS (spectrum) repo, these are not for compute-then-check CoT (reasoning stays empty).

Configs

load_dataset("AI4Manufacturing/IMS-perception", "spectrogram")

config	records	splits
`spectrogram`	631	{'train': 493, 'test': 138}
`scalogram`	631	{'train': 493, 'test': 138}
`waveform`	631	{'train': 493, 'test': 138}
`reshaped`	631	{'train': 493, 'test': 138}

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, set, timestamp, time_frac, channel, bearing, bearing_group, rpm, fs, fr_hz, features, fault_freqs, computed_verdict, computed_snr, evidence_tier, image_sha256, split

Provenance & reproducibility

Generated deterministically by forge_agent/examples/ims/convert.py (5622dddd61) → forge_model/IMS/convert_ims.py (2fb49936b1); see provenance.json.

Gold = the documented end-state defect (readme / manufacturer teardown): Set 1 → bearing 3 inner-race + bearing 4 roller(ball); Set 2 → bearing 1 outer-race; Set 3 → bearing 3 outer-race. normal = the early files of each run; fault = the late files of the failed bearing (per-set window from the degradation onset). A computed evidence_tier (confirmed/weak/absent) flags detectability.

Caveats

Evidence-gated release — every image visibly supports its label. IMS faults are WEAK run-to-failure signatures (the dataset's own reference paper, Qiu/Lee/Lin JSV 2006, studies weak-signature detection), so we curate by a computed evidence_tier: the spectrum/reasoning track keeps only confirmed records (the fault peak is actually present → faithful compute-then-check CoT); the perception tracks keep confirmed+weak and drop absent.
inner_race is EXCLUDED from this release. IMS's Set-1 inner-race defect is a weak, multi-fault-mixed signature with too few detectable spectra to form a class (a handful of confirmed records). It is retained in the raw form (evidence_tier intact) for full transparency, but not published as a class. Published classes: normal / outer_race / ball — outer_race (Sets 2-3) is the clean, strong class; ball (Set-1 roller) is smaller and also weak.
Few distinct bearings — each fault class comes from one run-to-failure bearing, so a strict bearing-wise split is impossible within a class; the split is file-stratified. Treat cross-bearing generalization claims with care.

Source & license

Source: IMS / NASA-Bearing — NSF I/UCR Center for Intelligent Maintenance Systems (imscenter.net) with Rexnord Corp.; three test-to-failure runs on Rexnord ZA-2115 bearings at 2000 rpm. Reference: H. Qiu, J. Lee, J. Lin, J. Sound and Vibration 289 (2006) 1066–1090. Distributed via the NASA Prognostics Data Repository.