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Wind Turbine Gearbox Condition Monitoring Vibration Analysis Benchmarking Datasets

Metadata Updated: April 26, 2022

Wind turbine condition monitoring (CM) can potentially help the wind industry reduce turbine downtime and operation and maintenance (O&M) cost. NREL CM research has investigated various condition-monitoring techniques such as acoustic emission (AE specifically stress wave), vibration, electrical signature, lubricant and debris monitoring based on the Gearbox Reliability Collaborative dynamometer and field tests, and other test turbines and resources accessible by NREL. During the past several years, NREL CM research has shown that there are very few validation and verification efforts on commercial wind turbine CM systems. One of the reasons might be limited benchmarking datasets accessible by stakeholders. To fill this gap, NREL executed a data collection effort. The targeted users of these datasets include those investigating vibration-based wind turbine CM research, evaluating commercially available vibration-based CM systems, or testing prototyped vibration-based CM systems.

NREL collected data from a healthy and a damaged gearbox of the same design tested by the GRC. Vibration data were collected by accelerometers along with high-speed shaft RPM signals during the dynamometer testing. The healthy gearbox was only tested in the dynamometer. The damaged gearbox was first tested in the dynamometer and later sent to a wind farm close to NREL for field testing. In the field test, it experienced two loss-of-oil events that damaged its internal bearings and gear elements. The gearbox was brought back to NREL and it was retested in the dynamometer with CM systems deployed under controlled loading conditions that would not cause catastrophic failure of the gearbox.

The objective of releasing these datasets to the public along with information about the real damage that occurred to the damaged gearbox is to provide the wind industry with some benchmarking datasets. These datasets will benefit research, development, validation, verification, and advancement of vibration-based wind condition-monitoring techniques.

By accessing this data you acknowledge the terms outlined in the "License Information" document.

Please contract Shawn Sheng (NREL) if you have any questions on the data or would like to collaborate on publications based on the datasets.

Access & Use Information

Public: This dataset is intended for public access and use. License: Creative Commons Attribution

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Metadata Created Date April 26, 2022
Metadata Updated Date April 26, 2022

Metadata Source

Harvested from OpenEI data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date April 26, 2022
Metadata Updated Date April 26, 2022
Publisher National Renewable Energy Laboratory
Doi 10.25984/1844194
Data First Published 2014-03-28T06:00:00Z
Data Last Modified 2022-02-09T16:24:41Z
Public Access Level public
Bureau Code 019:20
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Data Quality True
Harvest Object Id d4cb0dc3-7b55-4b08-9207-defe60e31cce
Harvest Source Id 7cbf9085-0290-4e9f-bec1-91653baeddfd
Harvest Source Title OpenEI data.json
Homepage URL
Old Spatial {"type":"Polygon","coordinates":-180,-83,180,-83,180,83,-180,83,-180,-83}
Program Code 019:010
Projectnumber FY14 AOP
Projecttitle Wind Turbine Drivetrain Reliability
Source Datajson Identifier True
Source Hash 57450f3e2db3c75d66cd41ddbb3eeef07ad5431c
Source Schema Version 1.1
Spatial {"type":"Polygon","coordinates":-180,-83,180,-83,180,83,-180,83,-180,-83}

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