Datasets:

AI4Manufacturing
/

MFPT

license: cc-by-nc-sa-4.0
task_categories:
  - image-classification
pretty_name: MFPT Bearing Fault — Envelope Spectrum (signal→VLM, Category C)
tags:
  - bearing-fault-diagnosis
  - vibration
  - signal-to-image
  - mfpt
  - compute-then-check

MFPT — bearing fault classification from the envelope spectrum (reasoning track)

Part of the AI4Manufacturing FORGE corpus (Category C, task T-C1). Each record is the envelope spectrum of a 1 s rolling-element-bearing vibration window with the fault frequencies marked — the representation that supports faithful compute-then-check chain-of-thought. reasoning is empty here; the MFPT-annotated sibling fills it.

Records: 78 (splits {'train': 54, 'test': 24}); labels {'inner_race': 21, 'outer_race': 39, 'normal': 18}; evidence_tier {'confirmed': 78}.

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, features, fault_freqs, computed_verdict, computed_snr, evidence_tier, published_fault_hz, load_lbs, bearing, channel, fs, fr_hz, file, window_idx, image_sha256, split

Splits

train / test, following MFPT's own by-load-condition partition (leakage-safe: each file is wholly on one side; windows never cross files).

Provenance & reproducibility

Generated deterministically by forge_agent/examples/mfpt/convert.py (3245b6c799) → forge_model/MFPT/convert_mfpt.py (d74c16b01b); see provenance.json for the full record.

The MFPT test bearing's geometry (8 elements, d=0.235″, D=1.245″, 0° contact) is cross-checked against each file's own published fault frequencies: geometry-derived BPFO/BPFI agree to <0.001 Hz (BSF ~0.05 Hz rounding). FTF differs by convention (MFPT publishes the cage rate relative to the rotating inner race; forge_tools reports the standard train/cage frequency) — reported, not reconciled. Labels come from the dataset's own filenames; the shaft rate (25 Hz), sample rate and load are read from each .mat. An evidence gate (a label-independent envelope-spectrum detector) confirms every published image supports its label — on this clean, well-separated data every window is confirmed and every computed verdict matches its gold (baseline → healthy with zero false positives), so no record is dropped.

Caveats

Small, clean source — MFPT ships 20 lab files (3 baseline, 3+7 outer-race, 7 inner-race) at a single 25 Hz shaft speed. The value is a groundable, load-varying bearing benchmark (loads 0–300 lb) that complements CWRU/XJTU/IMS, not raw record volume.
No ball class — MFPT seeds only inner- and outer-race faults (+ healthy), so this is a 3-class task (unlike CWRU's 4).
Split — train/test follow the dataset's own by-load-condition partition (each file wholly on one side; windows never cross files, so it is leakage-safe). It is a condition split, not an unseen-bearing split (MFPT is one bearing type).
FTF convention — the published_fault_hz in metadata uses MFPT's cage-relative-to-inner FTF; the fault_freqs use forge_tools' standard train-frequency FTF. BPFO/BPFI (which drive the verdicts) agree in both.

Source & license

Source: Society for Machinery Failure Prevention Technology (MFPT) bearing fault data (orig. Eric Bechhoefer), obtained via MathWorks' RollingElementBearingFaultDiagnosis-Data distribution. License: CC BY-NC-SA 4.0 (Attribution — NonCommercial — ShareAlike): this derived dataset is redistributed under the same terms; non-commercial use only, with attribution to MFPT / Eric Bechhoefer and MathWorks.