Full-condition Digital Twin Dataset for Wind Turbine in Real Scenarios 核心 · 已核验

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This dataset takes cooling fans operating under actual working conditions as the research object. Centering on the requirements of digital twin modeling and signal processing, it carries out dynamic, real-time and continuous physical signal acquisition. The experimental environment is characterized by severe aliasing of strong aerodynamic noise and bearing fault signals, with heavy noise interference, which is highly consistent with real industrial field scenarios.

The dataset contains multi-type fault samples of SKF 6236 bearings: normal bearing (NM), outer race fault (OR), inner race fault (IR), rolling element fault (RF), and cage fault (CF). Different severity levels are set for each fault type. Some faulty bearings are equipped with nylon dust covers, while normal bearings retain the original metal dust covers. The rotational speed ranges from 150 rpm to 1500 rpm.

Featuring real working scenarios, complex environments, strong noise, abundant fault types and coverage of different fault degrees, this dataset can truly simulate the operating status of bearings under actual working conditions, providing high-difficulty and high-authenticity data support for digital twin-oriented bearing fault diagnosis research.

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落地页

https://zenodo.org/doi/10.5281/zenodo.19200999

许可证

CC-BY-4.0 (置信:verified_official)

国内可访问性

国内直连:直连超时(慢或不稳定,非封锁证据) (2026-07-11 检测) 非直连:可达 (2026-07-11 检测)
「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。

发布年份

2026

发布方

Zenodo

设备类型

wind_turbine rolling_bearing

PHM 任务

fault_diagnosis

故障工况

运行工况