Part of the AI4Manufacturing FORGE corpus (Category C, task T-C1). Each record is the stator-current spectrum zoomed around the supply line, in dB below it, with the predicted broken-rotor-bar sideband positions marked. It is the corpus's only dataset where the fault severity is a physically graded quantity — 1, 2, 3 or 4 bars actually drilled through. reasoning is empty here; the IEEE-BRB-annotated sibling fills it.

Records: 346 (splits {'train': 246, 'test': 100}); labels {'1_broken_bar': 50, '2_broken_bars': 70, '3_broken_bars': 76, '4_broken_bars': 74, 'healthy': 76}; evidence_tier {'confirmed': 346}.

By load (N.m): {0.5: 23, 1.0: 37, 1.5: 42, 2.0: 48, 2.5: 48, 3.0: 48, 3.5: 50, 4.0: 50} — the light loads are thin on purpose; see Caveats.

Rig

WEG W22 three-phase induction motor, 1 cv, 220/380 V, 4 poles, 60 Hz, 4.1 N.m and 1715 rpm nominal, squirrel-cage rotor of 34 bars, coupled to a DC generator that sets the load torque. Faults are physical: the rotor was drilled with a 6 mm bit (wider than a bar) at mid-length, and five rotors were tested — healthy, then 1, 2, 3 and 4 adjacent broken bars. Every run is a direct start from the mains with balanced three-phase supply. That detail matters: with no inverter there is no current controller regulating the rotor 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: healthy / 1_broken_bar / 2_broken_bars / 3_broken_bars / 4_broken_bars
`reasoning`	chain-of-thought (empty here; filled in the `IEEE-BRB-annotated` sibling)
`cate` / `task`	`C` / `T-C1` (signal fault classification)
`metadata`	JSON string: representation, `sideband_margin_db` and the per-line LSB/USB dB and Hz, slip + its source, offset in Hz and FFT bins, `resolvable`, the per-load `margin_threshold_db`, computed_verdict, evidence_tier, load_nm, repetition, line_hz, fs, file, image_sha256, split

Splits

train / test = repetitions 1-7 / 8-10 (leakage-safe: a run is never split).

Provenance

Generated deterministically by forge_agent/examples/ieee_brb/convert.py (6ceba43daf) → forge_model/IEEE_BRB/convert_ieee_brb.py (6e138e37d4); see provenance.json for the full record.

How the evidence is measured

A broken bar makes the rotor magnetically asymmetric, which modulates the stator current and puts a pair of lines at f_line·(1 ± 2ks). The measured scalar is how far below the supply line the stronger of them sits (sideband_margin_db; smaller = stronger fault).

Two rules make that measurement honest, and both are there because the first build got them wrong:

The sideband position is predicted, never searched for. Allowed to roam, a peak search finds a "sideband" in perfectly healthy data — it locks onto the supply line's own leakage skirt. Measured at 4.0 N.m, a free search read healthy 39.8 dB and 1-broken-bar 40.2 dB (indistinguishable, both implying an impossible 0.57 % slip); anchoring on the nameplate slip instead gave 47.0 vs 41.2 dB. The slip comes from registry.get_motor() scaled by the recorded load — an operating condition, not the label.
The search window scales with the offset. The distance from the line to the first sideband is 2·s·f_line, which collapses from 5.5 Hz at full load to 0.69 Hz at 0.5 N.m. A fixed ±1.5 Hz window is then wider than the whole offset and swallows the supply line itself.

The gate

A run "shows fault evidence" when its margin falls below that load's threshold, and the threshold is mean − 3·std over that load's train-split healthy runs only:

load	threshold
0.5 N.m	34.0 dB
1.0 N.m	34.5 dB
1.5 N.m	36.5 dB
2.0 N.m	37.0 dB
2.5 N.m	38.0 dB
3.0 N.m	39.6 dB
3.5 N.m	42.7 dB
4.0 N.m	44.9 dB

Caveats

The evidence gate is measured on the SIGNAL, not on the image. mcsa_sidebands reads the raw 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". 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 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. When it is, the rule is: evidence missing from the signal → drop the record; evidence present but unreadable in the image → change the rendering. This dataset already follows the second half — the zoomed dB spectrum exists because the unzoomed one hid the sidebands entirely, and the four perception renders are documented as unable to show the fault rather than dropped.
The fault is only visible under load. This is the dataset's defining property, measured across all 400 runs. At 3.0 N.m and above all four fault levels separate from healthy with no overlap; at 0.5 N.m nothing separates, because a lightly loaded motor barely slips and the sidebands crowd the supply line. Nothing hard-codes a load cutoff: the per-load evidence gate simply finds less and less to confirm, and the reasoning track thins out accordingly (confirmed runs by load: 0.5 N.m 23/50, 1.0 37/50, 1.5 42/50, 2.0-3.0 48/50, 3.5-4.0 50/50).
The marked sideband positions are PREDICTED, and the real peaks sit slightly closer to the supply line. The nameplate slip runs ~15-25 % high, so the measured peak is 0.19 Hz (0.5 N.m) to 0.78 Hz (4.0 N.m) inside the marker. The peak search window absorbs that at every load, but at 0.5 N.m the peak lands 92 % of the way to the window edge — which is the mechanical reason the light loads are fragile. A per-load slip calibration would remove the bias and is the obvious next improvement; this build ships the nameplate slip and states the offset.
"Separable" has two senses here and only one holds. Each fault class against healthy separates with no overlap at 3.0/3.5/4.0 N.m (4 of 4 classes). Adjacent fault classes do not separate at any load: the best cases, 3.5 and 4.0 N.m, reach 3 of 4, and 3.0 N.m only 2 of 4. At 4.0 N.m one of the ten 4-broken-bar runs reads 44.0 dB against 29-31 dB for its siblings — a healthy-looking outlier that happens to sit in the test split. So no record's evidence supports the bar count; every confirmed record supports only "a fault is present".
The verdict is binary; the gold is the bar count. The margin is monotone in the count (measured at 4.0 N.m: healthy 45–49 dB, 1 bar 40–42, 2 bars 34–37, 3 bars 32–33, 4 bars 29–31), but the detector is only asked to attest that evidence is present — grading to an exact count would need four label-derived thresholds. Same shape as the SEU gearset release.
Per-load calibration. A healthy rotor's margin depends strongly on load, so the threshold is computed per load group from that load's train-split healthy runs only (mean − 3·std) and applied blind. No threshold was adjusted against the faulted rotors.
No windowing. One 20.02 s run is one record. MCSA resolution is the whole game: the full record gives 0.05 Hz bins, and halving the window would halve that — at light load the sidebands are already only ~14 bins from the supply line.
Split — train/test follow repetitions 1-7 / 8-10. A run is never split, so it is leakage-safe. A rotor-wise split would be the strong one and is not available: each class is exactly one physical rotor, and all 8 loads of that rotor sit in the data, so a model may learn the rotor rather than the fault. Same shape of limitation as the SEU release (one specimen per class/condition).
Adjacent rotors. The broken bars are always adjacent, which is the easier case; scattered breaks produce weaker sidebands and are not represented here.
Two points where the source page and the data disagree, both recorded rather than guessed. The page says each run is 18 s; the files measure 1,001,000 samples at 50 kHz = 20.02 s, and the data is what is used. The page describes the eight load levels as 12.5–100 % of full load (0.5125–4.1 N·m) while the in-file group names torque05–torque40 are read as 0.5–4.0 N·m; at full load that 4.0-vs-4.1 gap is a 2.4 % slip difference = 0.13 Hz in the predicted sideband position, against a ±1.5 Hz search window, so no verdict changes. The upstream readme.txt would settle it and has not been retrieved.

Source & license

Source: Experimental database for detecting and diagnosing rotor broken bar in a three-phase induction motor, Laboratory of Intelligent Automation of Processes and Systems (LAIPS) and Laboratory of Intelligent Control of Electrical Machines (LACIME), School of Engineering of São Carlos, University of São Paulo, Brazil — IEEE DataPort, doi:10.21227/fmnm-bn95.

License: CC BY 3.0 US (Attribution). Checked 2026-08-05: the dataset page itself names no licence; the licence comes from IEEE DataPort's Terms of Use, which place uploaded content under the Creative Commons Attribution (CC BY) License, 3.0 US. CC BY permits redistribution and derivative works with attribution, so this derived dataset is redistributed under the same terms.

Please cite the source dataset:

Aline Elly Treml, Rogério Andrade Flauzino, Marcelo Suetake, Narco Afonso Ravazzoli Maciejewski, Experimental database for detecting and diagnosing rotor broken bar in a three-phase induction motor., IEEE Dataport, September 16, 2020, doi:10.21227/fmnm-bn95

Downloads last month: -

Total file size:

13.3 MB