# hustcxl/Rotating-machine-fault-data-set

Open rotating mechanical fault datasets (开源旋转机械故障数据集整理)

Repository: https://github.com/hustcxl/Rotating-machine-fault-data-set
Canonical: https://ross.abutalabs.com/products/rotating-machine-fault-data-set
License: MIT
License Family: permissive
Topics: rolling-element-bearings, condition-monitoring, prognostics, fault-data, femto-st, mfpt, rotating-machinery-fault-diagnosis
Last push: 2026-04-09T08:45:49+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 76, release rhythm 35, longevity 100
- inputs: {"age_days": 2694, "days_push": 146, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1212, forks 307 (observed 2026-08-28T04:04:00.340066+00:00)

## What it is
A curated collection of open rotating machinery fault diagnosis datasets, including bearing and gearbox vibration data from CWRU, MFPT, Paderborn, FEMTO-ST, IMS, XJTU-SY, and many other academic sources. It organizes download links and usage notes rather than hosting the raw data itself.

## Use cases
- find bearing vibration datasets for fault diagnosis research
- download CWRU bearing dataset for machine learning experiments
- get bearing degradation data for remaining useful life prediction
- compare gearbox fault datasets for deep learning models
- find open data for PHM and condition monitoring research
- locate MAFAULDA or Paderborn bearing dataset download links

## When to choose
- you need benchmark vibration datasets for bearing or gearbox fault diagnosis
- you are starting PHM research and need standard datasets like CWRU or FEMTO-ST
- you want a single index of many open rotating machinery fault data sources

## When to avoid
- you need the raw data hosted directly in the repository
- you need non-rotating-machinery sensor or image datasets
- you need ready-made trained models or diagnosis code

## Facets
- artifact type: dataset
- maturity: active
- function: data-science, machine-learning, analytics
- domain: machine-learning, data-science, simulation
- platform: cross-platform
- tags: bearing-fault-diagnosis, vibration-signals, condition-monitoring, phm, rotating-machinery, curated-list, prognostics

## Member repositories
- hustcxl/Rotating-machine-fault-data-set (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:00.340066+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:17:59.736199+00:00, confidence not recorded.
  - readme: https://github.com/hustcxl/Rotating-machine-fault-data-set (fetched 2026-08-28T04:04:00.340066+00:00, sha 9ad50c89eab7)
- Data as of 2026-08-30T08:39:29.467469+00:00.