ITSC — stator inter-turn short circuit from the three-phase current locus (reasoning track)

Part of the AI4Manufacturing FORGE corpus (Category C, task T-C1), and the corpus's first motor-current dataset. Each record is the Park-vector locus of a 0.5 s window of three-phase stator current — the (i_d, i_q) point traced over time, drawn against an equal-area reference circle with the A/B/C phase axes marked. The plot has four layers: the pale trace is the raw locus itself (~30 revolutions in 0.5 s — they overlap into a thin halo when the current is steady, and fan out into visible separate loops when its amplitude drifts within the window); the dashed grey circle is the equal-area reference; the solid dark-blue curve is the fitted ellipse the measurement is taken from; the red line is its major axis. A balanced winding traces a circle; an asymmetric one traces an ellipse whose major axis points at the faulted phase. That is the representation that supports faithful compute-then-check chain-of-thought. reasoning is empty here; the ITSC-annotated sibling fills it.

Records: 183 (splits {'train': 111, 'test': 72}); labels {'phase_C': 60, 'phase_B': 54, 'phase_A': 54, 'normal': 15}; evidence_tier {'confirmed': 183}.

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: windows never cross a recording).

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.

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.
• One crop residue. In SC_A4_B0_C0_004 window 2 the short is switched out 0.22 s into the window: the Park modulus' oscillation collapses from 52% of its mean to 8.7% (ellipse -> near-circle) and the last ~36% of the window is fault-free. It still clears the evidence gate (ellipticity 31.3 vs threshold 8.5) and its label is unaffected, but its pale locus is visibly fanned, and it is the only faulted record whose ellipticity varies more than 20% across its own windows (24.9%; next worst 9.6%, median 1.8%). Left in deliberately — the label is the authors' own and it is correct.
• 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.