Datasets:
Roles
Roles: canon repo — annot is the source label, kept machine-parseable as the gold for verification and reward parsing; there is no filled reasoning column and this repo is not itself a training view. Derived repos each state their own regime on their own card.
XJTU-SY — fault classification from the envelope spectrum (reasoning track)
Third signal dataset in the AI4Manufacturing FORGE corpus (Category C, task T-C1), from 15 accelerated run-to-failure tests. Each record is the envelope spectrum of a 1.28 s vibration snapshot with the characteristic fault frequencies marked — the representation for faithful compute-then-check CoT. reasoning is empty; the XJTU-annotated sibling fills it.
Records: 907 (splits {'train': 690, 'test': 217}); labels {'normal': 416, 'outer_race': 352, 'inner_race': 139}; evidence_tier {'confirmed': 907}.
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 fault class: normal / inner_race / outer_race |
reasoning |
chain-of-thought (empty here; filled in the -annotated sibling) |
cate / task |
C / T-C1 (signal fault classification) |
metadata |
JSON string: representation, condition, bearing_id, file_number, time_frac, life_files, channel, bearing, rpm, fs, fr_nominal, fr_used, fr_source, features, fault_freqs, computed_verdict, computed_snr, evidence_tier, line_evidence, image_sha256, split |
Figures (style pool)
The spectra in this repo come from the figure-style pool: each record is drawn in one of 11 styles. What the style varies: colours, canvas, title placement and presence, tick spacing and minor ticks, grid, spines, fill, the fault-frequency marker drawn as a line or as a band, label placement and orientation, one colour per family plus a legend, font family, scientific notation, and tick direction. The style is picked by sha256("figstyle|" + image_path) % 11 -- deterministic, reproducible, and drawn independently of the opener / knowledge / skeleton pools.
What never varies is the content: the curve; the plotted band; which lines are marked (six on these figures -- the four families plus 2xBPFO and 2xBPFI); and the y-limits, autoscaled once per record and reused by every style, so a peak is the same height whichever style drew it. No threshold bar and no noise-floor line is ever drawn, and no marker is styled differently for being the answer. The point is that a model cannot pass by memorising one house plotting style. The -perception repo's images are unchanged.
Three things made these figures and no single pin covers all three, so provenance.json carries one for each:
- Signals and manifest -- forge_agent
0574e0e25d. - The curve and the producer's plotting parameters -- forge_agent
733a683. This pin fixes what is plotted; it does not produce the image bytes below. - The published image bytes --
figure_manifest_sha256=203e42bb5cd0491fa8a61faf569d0891c3f44db62d5b3b07b2961571e791c2db, the roll-up of one sha256 per image across the whole style pool. The style layer that draws them (_figstyle_corpus/_src/style_pool.py) lives outside this repository and is not under version control, and the pool has been re-rendered since the commit above -- so this checksum, not a commit, is what identifies these exact images.
Provenance & reproducibility
Generated deterministically by forge_agent/examples/xjtu/convert.py (0574e0e25d) → forge_model/XJTU/convert_xjtu.py (4b4cda0231); see provenance.json.
Gold = the documented teardown failure element (Table 3 of the dataset paper): outer_race = bearings 1_1/1_2/1_3/2_2/2_4/2_5/3_1/3_5, inner_race = 2_1/3_3/3_4. normal = early files (before min(30% of life, the data-driven degradation onset)); bearings whose onset is floor-bound (degrading from day one) contribute no normals. Each record was scored under both the nominal and a spectrum-refined shaft rate (rigs deviate 0.5–2% from nominal); the better envelope-pattern match won.
Caveats
- Evidence-gated, conflict-free release. The reasoning track keeps only
confirmedrecords (the label-independent envelope-spectrum detector independently finds the documented fault). The perception tracks keepconfirmed+ non-conflictingweak; records where the detector confidently found a different pattern than the gold (e.g. bearing 2_5's healthy shaft harmonic aliasing into BPFI within 1.7%) are dropped — an image should never fight its own label. cageis EXCLUDED from this release. XJTU has two cage-failure bearings, but only 6/142 files confirm a cage (FTF-ladder) signature — and 57 fault files score as outer_race instead (a failing cage hammers the outer raceway; 8×FTF ≡ BPFO for this geometry). Retained in the raw form; the cage bearings' certified-healthy early files still serve asnormal. Published classes: normal / inner_race / outer_race.- TRUE bearing-wise split — the first run-to-failure set with enough bearings for it: test = whole held-out bearings (1_3, 2_5 outer; 3_3 inner; plus cage bearing 2_3, which after the cage exclusion contributes early-life
normalrecords only), so evaluation is on unseen bearings;normalappears in both splits from disjoint bearings. Compound-failure bearings (1_5, 3_2) appear only as early-lifenormal. - End-of-life masking — in the final ~3% of life, broadband breakdown can mask the discrete fault comb, so late windows are not uniformly
confirmed. A property of the physics, not the converter.
Source & license
Source: XJTU-SY bearing datasets — Xi'an Jiaotong University & Changxing Sumyoung Technology; 15 LDK UER204 bearings run to failure under 3 conditions (2100/2250/2400 rpm, 12/11/10 kN); horizontal-channel accelerometer snapshots (25.6 kHz, 1.28 s per minute). Cite: B. Wang, Y. Lei, N. Li, N. Li, IEEE Trans. Reliability 69(1):401–412, 2020 (DOI 10.1109/TR.2018.2882682). Gold labels: Table 3 of Lei et al., J. Mech. Eng. 55(16), 2019 (DOI 10.3901/JME.2019.16.001). Released by the authors for research use (biaowang.tech/xjtu-sy-bearing-datasets).
Overlap / de-duplication (§8)
Cross-family evaluation lock — metadata.eval_lock (stamped 2026-09-20; manifest revision fe6e286912b0, generated 2026-09-08). Every record of this repository, locked or not, carries metadata.eval_lock, computed by forge_model/common/overlap.py::Overlap.stamp_for against common/overlap_manifest.json at that revision — so within this repository the absence of the key cannot occur. Shape: {"locked": bool, "against": [{"repo": …, "split": …}, …], "own_split": …, "manifest_revision": …, "manifest_generated": …}. locked is true when the image is evaluation material anywhere in the corpus; against names every repository and split in which it is (sorted; [] when not locked; it includes the record's own family where that is so); own_split marks a record locked by its own split. The per-record field is the authority — the count here is quoted once, at this revision, and a later manifest may change it: 217 of 907 records (217 distinct images) are locked — by column: 0 by the cross-family manifest, 217 by their own split, 0 both ways and counted once; counterparts (records per counterpart; a record can appear under several): none — every lock here is by the record's own split; 217 locked by their own split: test. In words: 217 of the 907 records in this repository are evaluation material by their own metadata.split (test: 217) and sit inside the HF split named test / train — under the uniform-split convention the HF split name is a container name, and metadata.split together with metadata.eval_lock carries the truth; a train pool must exclude them. A stamp whose manifest_revision differs from the current manifest is stale, not wrong — recompute it (Overlap.stamp_is_current); a record with no stamp has not been checked against the corpus as it now is. Overlap.partition / assert_train_pool_clean read the field: a train pool built from this repository must exclude every locked record.
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