README.md

metadata

dataset_info:
  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: 27980365
      num_examples: 409
    - name: test
      num_bytes: 6820617
      num_examples: 100
  download_size: 34398301
  dataset_size: 34800982
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
pretty_name: IMS/NASA-Bearing — Envelope Spectrum (signal→VLM, Category C)
tags:
  - bearing-fault-diagnosis
  - vibration
  - signal-to-image
  - ims
  - nasa
  - run-to-failure
  - compute-then-check
license: cc-by-4.0
task_categories:
  - image-classification

IMS / NASA-Bearing — fault classification from the envelope spectrum (reasoning track)

Second signal dataset in the AI4Manufacturing FORGE corpus (Category C, task T-C1), from three run-to-failure experiments. Each record is the envelope spectrum of a 1 s vibration window with fault frequencies marked — the representation for faithful compute-then-check CoT. reasoning is empty; the IMS-annotated sibling fills it.

Records: 509 (splits {'train': 409, 'test': 100}); labels {'normal': 239, 'ball': 30, 'outer_race': 240}; evidence_tier {'confirmed': 509}.

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.