--- license: cc-by-4.0 task_categories: - image-classification pretty_name: UORED-VAFCLS Bearing — Perception Representations (signal→VLM) tags: - bearing-fault-diagnosis - vibration - acoustic - signal-to-image - uored-vafcls --- # UORED-VAFCLS — perception representations (visual grounding) 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 ```python 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__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 We do not publish our own holdout: any particular choice would read as the only defensible one, and the honest constraint is the grouping rule, not one instance of it.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](https://doi.org/10.1016/j.dib.2023.109327). Data: doi:[10.17632/y2px5tg92h.5](https://doi.org/10.17632/y2px5tg92h.5), **CC BY 4.0**.