---
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: 27980365.0
    num_examples: 409
  - name: test
    num_bytes: 6820617.0
    num_examples: 100
  download_size: 34398301
  dataset_size: 34800982.0
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` (`8f7c384bda`) → `forge_model/IMS/convert_ims.py` (`a88573f32f`); 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.