AI4Manufacturing/ITSC-perception

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

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.

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). 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.