---
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: 2277570.0
    num_examples: 488
  - name: test
    num_bytes: 567066.0
    num_examples: 121
  download_size: 2564822
  dataset_size: 2844636.0
- 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: 70256736.0
    num_examples: 488
  - name: test
    num_bytes: 17426538.0
    num_examples: 121
  download_size: 87310010
  dataset_size: 87683274.0
- 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: 58813528.0
    num_examples: 488
  - name: test
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    num_examples: 121
  download_size: 73023951
  dataset_size: 73404876.0
- 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: 20236425.0
    num_examples: 488
  - name: test
    num_bytes: 5067573.0
    num_examples: 121
  download_size: 24878529
  dataset_size: 25303998.0
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-*
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
```python
load_dataset("AI4Manufacturing/IMS-perception", "spectrogram")
```

| config | records | splits |
|---|---|---|
| `spectrogram` | 609 | {'train': 488, 'test': 121} |
| `scalogram` | 609 | {'train': 488, 'test': 121} |
| `waveform` | 609 | {'train': 488, 'test': 121} |
| `reshaped` | 609 | {'train': 488, 'test': 121} |

## 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 / 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` (`250c7e5f89`) → `forge_model/IMS/convert_ims.py` (`229ee98152`); 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, conflict-free release — every image supports (and never fights) 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 label-independent envelope-spectrum detector independently finds the documented fault → faithful compute-then-check CoT); the **perception** tracks keep `confirmed` + non-conflicting `weak` — weak records where the detector confidently found a **different** pattern than the gold are dropped (notably ball windows scoring as cage: ball faults are cage-modulated, so the single-label gold and the detector legitimately disagree there).
- **`inner_race` is the scarce class — excluded from the first release, REINSTATED here.** The original band-limited detector confirmed only ~8 inner-race spectrum windows ("too few to form a class"); the current full-band demodulation search recovers IMS's weak Set-1 signature and confirms **36** windows — more than the published `ball` class (30) — so the same evidence standard that excluded it now reinstates it. It remains the weakest class: ~78% of its raw windows are `absent`-tier (dropped per-record by the gate), and Set 1 ran two degrading bearings (inner-race B3 + ball B4) on one shaft. `outer_race` (Sets 2-3) is the clean, strong class.
- **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.