README.md

---
license: mit
task_categories:
- image-classification
pretty_name: ITSC Stator Fault — Perception Representations (signal→VLM, Category C)
tags:
- motor-fault-diagnosis
- stator-fault
- signal-to-image
- perception
configs:
- config_name: spectrogram
  data_files:
  - split: train
    path: spectrogram/train-*
  - split: test
    path: spectrogram/test-*
- config_name: scalogram
  data_files:
  - split: train
    path: scalogram/train-*
  - split: test
    path: scalogram/test-*
- config_name: waveform
  data_files:
  - split: train
    path: waveform/train-*
  - split: test
    path: waveform/test-*
- config_name: reshaped
  data_files:
  - split: train
    path: reshaped/train-*
  - split: test
    path: reshaped/test-*
---
# ITSC — stator fault, perception representations (grounding track)

Part of the AI4Manufacturing FORGE corpus (Category **C**, task **T-C1**). Four image renderings of the **Park modulus** |i_dq| — one channel derived from all three phase currents, so a single-channel view still carries the asymmetry (a balanced set gives a constant modulus; an unbalanced one ripples at twice the line frequency).

**Records:** 756 across 4 configs (189 windows each); labels {'phase_C': 60, 'phase_B': 57, 'phase_A': 57, 'normal': 15}.

| config | image |
|---|---|
| `spectrogram` | short-time Fourier transform (time × frequency) |
| `scalogram` | Morlet continuous-wavelet transform (time × scale) |
| `waveform` | the raw time trace |
| `reshaped` | samples arranged into a 2-D grayscale grid |

```python
from datasets import load_dataset
ds = load_dataset("AI4Manufacturing/ITSC-perception", "spectrogram")
```

The **reasoning** counterpart — the Park-vector locus, which is the representation the physics is read from — lives in [`AI4Manufacturing/ITSC`](https://huggingface.co/datasets/AI4Manufacturing/ITSC).

## Answer space: use these for *is there a fault*, not for *which phase*

`annot` carries the four-way gold (`normal` / `phase_A` / `phase_B` / `phase_C`) so the label is not lost, but **these four images cannot support the phase**, and that is structural rather than a rendering choice. They are drawn from the Park **modulus**, and the modulus is direction-blind: the shorted phase is encoded in the *orientation* of the locus, which taking |i_dq| discards. On the shipped features, predicting the phase from the major-axis direction gives **99 %**; from the ellipticity — the only phase-relevant quantity the modulus retains — it gives **33 %, i.e. chance**.

**Measured on the images too.** A small ViT (0.55 M params, 40 epochs × 3 seeds) trained from scratch on the pixels alone, balanced accuracy on held-out data:

| view | fault vs healthy, old settings | **new settings** | which phase |
|---|---|---|---|
| `scalogram` | 82 % | **100 %** | 31 % |
| `spectrogram` | 85 % | **90 %** | 33 % |
| `reshaped` | 71 % | **75 %** | 24 % |
| `waveform` | 73 % | 73 % *(settings unchanged — a control)* | 47 % |
| *the locus, for contrast* | — | — | ***68 %*** |

Chance is 50 % / 33 %; the physics reference is 100 % / 99 %. The phase column is measured with severities **held out** (train 10 %/20 %, test 30 %/40 %) rather than with the shipped repetition split: all twelve conditions appear on both sides of a repetition split, so a model can score by recognising the condition instead of the phase — which inflates `waveform` from 47 % to 72 % and `scalogram` from 31 % to 50 %. Under the honest split every one of the four collapses to chance while the locus does not.

So: **train and report binary on this track**, and take the phase from the locus in the reasoning repo. The four-way gold is kept as metadata.

**On the render settings.** The toolkit defaults were tuned for broadband bearing vibration; on a narrowband 1 kHz Park modulus they gave a spectrogram with only 4 time columns, a scalogram axis pinned to `fs`/128…`fs`/2 rather than to any chosen band, and a reshaped image that was **88 % zero padding** (500 samples padded to 64²). All are now set explicitly — see `_provenance.json` → `generation.perception_render`. `waveform` needed no change (500 points on an ~880 px canvas), which makes it a useful control: it returns the same 73 % under both, so the differences elsewhere are real rather than run-to-run noise.

One weakness to keep in mind: the dataset holds only **five healthy records**, two of which land in test — so the healthy side of every number above rests on 12 windows and moves in steps of 8 points.

## Rig
Baldor CM3542 three-phase squirrel-cage induction motor, 0.75 hp, 208-230/460 VAC, 1725 rpm at 60 Hz, 59 turns per pole, double-star. Inter-turn short circuits are seeded in one phase at a time at four severities; every measurement is a steady state **without load, fed directly from the mains**. That last detail is what makes the fault observable in the current at all — on an inverter-fed machine the drive's current controller regulates the asymmetry away.

## 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 class: normal / phase_A / phase_B / phase_C |
| `reasoning` | chain-of-thought (empty here; filled in the `ITSC-annotated` sibling) |
| `cate` / `task` | `C` / `T-C1` (signal fault classification) |
| `metadata` | JSON string: representation, ellipticity/major-axis/negative-sequence features, Park-modulus time stats, per-phase RMS, the calibrated thresholds, computed_verdict, evidence_tier, fault_phase, severity_pct, line_hz, fs, file, window_idx, image_sha256, split |

## Splits
`train` / `test` = repetitions 1-3 / 4-5 (leakage-safe).

## Provenance & the evidence gate
Generated **deterministically** by `forge_agent/examples/itsc/convert.py` (`6ceba43daf`) → `forge_model/ITSC/convert_itsc.py` (`6e138e37d4`); see `provenance.json` for the full record.

Labels come from the dataset's own filenames. An **evidence gate** — a label-independent computation on the raw current — decides what ships: `park_vector_negseq` measures the **ellipticity of the three-phase current locus** and the **direction of its major axis**, and a record is `confirmed` only when that blind computation lands on the gold class.

Both thresholds are calibrated on the **train split's healthy records only** and then applied blind: the ellipticity cut is `mean + 3·std` = **8.48%** (healthy 4.89 ± 1.19%, n=9 windows — thin, and stated as such below), and the phase-axis centres are the circular means of the major-axis angle per faulted phase (A=140.0°, B=89.5°, C=27.4°). **No threshold was adjusted against the faulted classes.**

A third check verifies Kirchhoff's law on each window (`ia+ib+ic ≈ 0`) against a fixed, physics-derived cut: inverting one current clamp doubles that residual, and every healthy rig measured sits far below the cut. A reversed clamp would otherwise read as a large false asymmetry — which is exactly what happens on the KAIST PMSM set. Here **0 of 195 windows** are flagged.

This track keeps **confirmed + weak-nonconflict** — a window is dropped only when the blind computation points at a *different* fault than the gold, never merely for being quiet.

## Caveats
- **The evidence gate is measured on the SIGNAL, not on the image.** `park_vector_negseq` reads the raw three-phase current; it never opens the PNG. So `evidence_tier` says *this signal supports the label* — **not** *this image shows it*. Every record carries `evidence_measured_on: "signal"` so the claim travels with the data. Gating this way is deliberate: the computation is deterministic and will give the same answer in a year, whereas filtering by what a model can already read would select for what it already knows. It is also **sound but incomplete** — the image is a lossy function of the signal, so no-evidence-in-signal does imply no-evidence-in-image, but a record can pass and still lose its evidence in rendering. **Image-side readability has never been measured**, here or anywhere in this corpus. When it is, the rule is: evidence missing from the signal → drop the record; evidence present but unreadable in the image → **change the rendering**, not the record set.
- **Severity is metadata, not the answer.**The source seeds four levels (10/20/30/40%) and they are carried per record, but adjacent levels overlap badly in every measured indicator, so a record cannot be graded to its exact percentage from this evidence. The answer space is therefore presence + location (`normal` / `phase_A` / `phase_B` / `phase_C`).
- **Source variant matters.** This build uses the authors' own per-record crop (`Cropped_Signals_SF/`), *not* the raw 5 s recordings. In the raw files the short circuit is switched in and out mid-record — the first and last second carry no fault — so windowing them would inherit a record label onto fault-free windows. Measured: 41% of faulted raw windows fall inside the healthy range, against 3% on the authors' crop.
- **Small, single-rig source.** 65 recordings from one 0.75 hp motor at one operating point (steady state, no load). The value is a *groundable* stator-fault benchmark, not record volume.
- **Ellipticity vs negative sequence — they are the SAME measurement here, and the choice is purely about the image.** On the 195 published windows the two are almost perfectly collinear (Pearson **r = 0.997**; neg-seq ≈ 0.60 × ellipticity, ratio spanning only 0.47–0.68) and they separate identically (each: 6 of 180 faulted inside the healthy range, 98.3 % best balanced accuracy). **Do not claim one is more robust than the other** — an earlier version of this card said ellipticity is less affected by supply-voltage unbalance; that is not supportable at r = 0.997. Ellipticity is used because it is **the quantity the published image actually draws**, so a chain-of-thought citing it cites something the reader can see; the negative-sequence ratio has no counterpart in the image.
- **No voltage channel.** Supply-voltage unbalance also produces an unbalanced current set, and with current-only data it cannot be separated from a winding fault. This limit applies to **both** indicators equally — it is a property of the dataset, not a reason to prefer one over the other.
- **The major-axis angle alone identifies the faulted phase for 95.0 % of faulted windows** (cluster means A 139.3° / B 87.0° / C 27.1°). It is only consulted after ellipticity has already said 'faulted' — healthy windows here cluster at 87-117°, overlapping phase B, so the angle is not evidence of a fault on its own.
- **Split** — `train`/`test` follow repetitions 1-3 / 4-5. Each repetition is a separate acquisition and windows never cross one, so it is leakage-safe. It is a *repetition* split on a single motor at a single operating point, **not** an unseen-machine split — it says nothing about transfer to another motor.
- **The healthy class is thin.** Five healthy recordings exist in total, three of them in train, so the ellipticity threshold stands on **9 windows**. That is the weakest link in this release.
- **Only the healthy-vs-faulted step is label-free.** Naming *which* phase uses the three phase-axis centres, which are the mean major-axis direction per faulted phase over the **train split's labels** — calibrated there, applied blind to test, but not label-independent. Do not read the whole verdict as label-free.

## Source & license
Source: **ITSC dataset**, Laboratory of Electrical Engineering, Universidad de Guanajuato, Mexico ([github.com/ibarram/ITSC](https://github.com/ibarram/ITSC)). **License: MIT.** Please cite: Cardenas-Cornejo, Ibarra-Manzano, González-Parada, Castro-Sanchez & Almanza-Ojeda, *Classification of inter-turn short-circuit faults in induction motors based on quaternion analysis*, **Measurement 222 (2023) 113680**, doi:10.1016/j.measurement.2023.113680.