Rolling-element bearing records from the University of Ottawa constant-load, constant-speed rig, rendered as perception images — one HF config per representation. Part of the AI4Manufacturing FORGE corpus (Category C, task T-C1).

480 records per config, from 20 physical bearings; labels {'ball': 80, 'cage': 80, 'normal': 160, 'inner_race': 80, 'outer_race': 80}; channels {'acc': 240, 'mic': 240}.

Configs

load_dataset("AI4Manufacturing/UORED-perception", "spectrogram")

config	records
`spectrogram`	480
`scalogram`	480
`waveform`	480
`reshaped`	480

⚠️ There is no train/test split — you must make one, and it must be BY BEARING

This dataset is shipped unsplit, deliberately. Every bearing is used for exactly one fault type:

bearings	class
1–5	`inner_race`
6–10	`outer_race`
11–15	`ball`
16–20	`cage`

Each bearing contributes one healthy record (H_<n>_0) and two fault records, and each record is cut into 4 windows × 2 sensor channels × 4 representations. A random split therefore puts windows from the same physical bearing — often the same 2.5 s window, merely rendered differently or read on the other sensor — on both sides, and the reported accuracy will be an artefact.

Note on the train split name. HuggingFace requires every split to be named, so the single unsplit set is served as train. It is the whole dataset, not a training portion — there is no matching test. load_dataset("AI4Manufacturing/UORED-perception", "spectrogram")["train"] returns all 480 records, and it is on you to divide them.

Group by metadata.bearing_id and hold out whole bearings, stratified across the four classes. Use metadata.record, metadata.channel and metadata.window_idx if you need finer grouping. We do not publish our own holdout: any particular choice would read as the only defensible one, and the honest constraint is the grouping rule, not one instance of it.

Why perception-only

reasoning is empty on every row and there is no -annotated sibling planned. Three of the four fault classes on this rig cannot be attributed from the signal, and the cause is the bearing's own geometry rather than the recording quality:

6203 has 8 balls, so the outer- and inner-race orders sit symmetrically about 4; and ball-diameter ÷ pitch-diameter = 0.2375 is within 5% of ¼. Together these put BPFO 3.0498× (1.63% from 3×), 2×BSF 3.9722× (0.70% from 4×) and BPFI 4.9502× (1.01% from 5×). A line matcher has to allow 1–2% for real bearing-to-bearing geometric scatter, so its window contains both the bearing line and the shaft harmonic — and shaft harmonics are present on healthy machines.
Measured without presupposing the answer: the brightest harmonic comb near the inner-race order sits at 4.998–5.036 (median 5.004) on 10/10 records — 0.1% from the integer, 0.9% from BPFI, with a spread of 0.8% while bearing-to-bearing scatter is 1–2%. Outer race lands at 3.078 and is attributable (7/10). Ball lands at 4.012. The cage line has no integer neighbour at all and its window is simply empty.
Ball has a second, documented cause: the dataset paper states "For ball fault data, no load was applied" — no load zone, the balls slip, and the impacts are not repeatable.

The defects themselves are real and detectable — a paired-baseline band-energy test separates 30/40 fault records at zero false alarms over 20 healthy records. What this dataset cannot support is the attribution step, which is exactly what a faithful chain-of-thought would have to perform. So the labels ship as implanted gold (where the rig operator installed the defect), the computed evidence rides along as non-gating metadata (evidence_is_gate: false on every row — nothing was dropped, relabelled or reordered by it), and no envelope-spectrum representation is rendered: an image with the bearing lines drawn on top of the shaft harmonics would invite exactly the confabulation this decision refuses.

⚠️ Two disclosures that affect how you should train and report

1. Bearings 16–19 are a different recording batch. They run at 1157–1172 rpm while every other bearing runs at 1769–1784 rpm, and their records carry roughly 30× the broadband RMS. The source paper documents a single constant nominal speed of 1750 rpm and does not mention this. Since those are four of the five cage bearings, amplitude or texture alone can identify the cage class without any bearing physics. Every row carries metadata.recording_batch; audit against it, and treat a high cage score with suspicion. This is recorded, not corrected — the cause is unknown.

2. The healthy records are "not yet broken", not "known good". The paper states they are taken from the first files of the same run-to-failure sequence, on bearings whose seals had been removed and which had been degreased to accelerate deterioration. They are a usable reference for the same bearing's own earlier state; they are not a clean negative control.

Other caveats

Both sensor channels are shipped (metadata.channel ∈ {acc, mic}): a PCB 623C01 accelerometer inside the housing and a PCB 130F20 microphone 2 cm away. Rows sharing a record and window_idx are the same physical event on two sensors — keep them together when splitting. The microphone is the stronger detector on this rig (30/40 vs 15/40 at the zero-false-alarm point), and the corpus has no other acoustic dataset.
The differential-temperature channel is not rendered and must not be used as an input feature: a single 6.8 °C threshold separates healthy from faulty 59/60, while the four fault classes' medians all fall within 21.9–26.1 °C. It encodes how long the rig ran, not what broke. It is summarised per row as d_temp_c with d_temp_leaks_label: true.
state (the _0/_1/_2 file suffix) is not a severity label. The three states are taken by file index within each run, not by measured damage size; only 7 of 20 pairs move in the expected direction. Do not train on it.
Renderer parameters are not the library defaults. Every default was calibrated on CWRU at 12 kHz; at 42 kHz the same code means something else. Decimating to 12 kHz to restore those numbers would have discarded 6–20 kHz, which is where this rig's bearing impacts live, so the full rate is kept and each renderer is given the resolution or slice it needs. The full conversion is in provenance.json under raw_form_provenance.render_calibration.

Provenance & reproducibility

Generated deterministically by forge_agent/examples/uored_vafcls/convert.py (4aab7b24dd) → forge_model/UORED/convert_uored.py (2e4f6ba156). Shaft speed is measured per record from the raw spectrum — the stamped rpm column in the source files points at empty spectrum, including on healthy records — using a 1–6 harmonic ladder that skips terms falling on mains harmonics. See provenance.json for the full record.

Source & license

Sehri, M. & Dumond, P., University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets, Data in Brief 49 (2023) 109327, doi:10.1016/j.dib.2023.109327. Data: doi:10.17632/y2px5tg92h.5, CC BY 4.0.

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