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signal_a1a
sequencelengths 1
1
| signal_a3a
sequencelengths 1
1
| signal_length
int32 4
4
| label
class label 4
classes | fault_condition
stringclasses 4
values | environmental_condition
stringclasses 3
values | sampling_frequency
float32 200
200
| wind_speed
float32 7
14
|
|---|---|---|---|---|---|---|---|
[[-0.6741170287132263,-0.7051479816436768,-0.7431319952011108,-0.7690590023994446,-0.770645022392273(...TRUNCATED) | [[-0.8243079781532288,-0.6970589756965637,-0.6048960089683533,-0.5698950290679932,-0.644434988498687(...TRUNCATED) | 4 | 0healthy
| healthy | EC1 | 200 | 7 |
[[-0.6747909784317017,-0.6770679950714111,-0.696923017501831,-0.7238799929618835,-0.7428060173988342(...TRUNCATED) | [[-0.7230920195579529,-0.6047809720039368,-0.6952850222587585,-0.7112360000610352,-0.500840008258819(...TRUNCATED) | 4 | 0healthy
| healthy | EC2 | 200 | 12 |
[[-0.5936629772186279,-0.7224149703979492,-0.8400999903678894,-0.8996319770812988,-0.875005006790161(...TRUNCATED) | [[-0.44429200887680054,-0.7264860272407532,-0.9914889931678772,-0.7133089900016785,-0.64494997262954(...TRUNCATED) | 4 | 0healthy
| healthy | EC3 | 200 | 14 |
[[-0.7442349791526794,-0.7449280023574829,-0.7418479919433594,-0.7353709936141968,-0.727600991725921(...TRUNCATED) | [[-0.8262159824371338,-0.6877099871635437,-0.6033719778060913,-0.5689060091972351,-0.645259976387023(...TRUNCATED) | 4 | 1main_bearing
| main_bearing | EC1 | 200 | 7 |
[[-0.09634009748697281,-0.20738500356674194,-0.3786799907684326,-0.5874729752540588,-0.8057879805564(...TRUNCATED) | [[-0.7272250056266785,-0.5992469787597656,-0.7008489966392517,-0.7081969976425171,-0.500295996665954(...TRUNCATED) | 4 | 1main_bearing
| main_bearing | EC2 | 200 | 12 |
[[-0.9690579771995544,-1.1270300149917603,-1.2226799726486206,-1.2588000297546387,-1.234619975090026(...TRUNCATED) | [[-0.45054399967193604,-0.7197989821434021,-0.979951024055481,-0.7119629979133606,-0.644571006298065(...TRUNCATED) | 4 | 1main_bearing
| main_bearing | EC3 | 200 | 14 |
[[-0.6360849738121033,-0.6945149898529053,-0.7683089971542358,-0.8169180154800415,-0.816452026367187(...TRUNCATED) | [[-1.2818599939346313,-1.2810900211334229,-0.9870510101318359,-0.4829249978065491,-0.014396299608051(...TRUNCATED) | 4 | 2high_speed_bearing
| high_speed_bearing | EC1 | 200 | 7 |
[[-0.7336000204086304,-0.7444679737091064,-0.7508389949798584,-0.7551760077476501,-0.74161297082901,(...TRUNCATED) | [[-1.0325499773025513,-1.7286200523376465,-0.8228989839553833,-0.9238420128822327,1.314039945602417,(...TRUNCATED) | 4 | 2high_speed_bearing
| high_speed_bearing | EC2 | 200 | 12 |
[[-0.5611649751663208,-0.7233909964561462,-0.8864679932594299,-0.970022976398468,-0.9282960295677185(...TRUNCATED) | [[-0.9711970090866089,-1.6780400276184082,-0.4331440031528473,-0.08105319738388062,0.145974993705749(...TRUNCATED) | 4 | 2high_speed_bearing
| high_speed_bearing | EC3 | 200 | 14 |
[[-0.6585040092468262,-0.7022309899330139,-0.7522280216217041,-0.785198986530304,-0.7895159721374512(...TRUNCATED) | [[-0.9227070212364197,-0.8593779802322388,-0.7151830196380615,-0.5989969968795776,-0.664898991584777(...TRUNCATED) | 4 | 3low_speed_planet_bearing
| low_speed_planet_bearing | EC1 | 200 | 7 |
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in Data Studio
5MW Wind Turbine Drivetrain Bearing Damage Dataset
Dataset Description
Dataset containing vibration signals from the NREL 5MW reference drivetrain model with different bearing conditions. The reference paper for this dataset is available at: DOI: 10.1016/j.renene.2022.12.049 This is a subset of the original dataset available at Zenodo. For the full dataset and additional information, please visit: DOI: 10.5281/zenodo.7674842
Features:
signal_a1a: Vibration time waveform from sensor A1Asignal_a3a: Vibration time waveform from sensor A3Asignal_length: Length of the signalslabel: Fault condition (0: healthy, 1: main bearing, 2: high-speed bearing, 3: low-speed planet bearing)environmental_condition: Operating conditions (EC1, EC2, EC3)sampling_frequency: 200.0 Hzwind_speed: Operating wind speeds (7.0, 12.0, 14.0 m/s)
Data Split
The dataset includes samples from:
- 4 different conditions (healthy + 3 fault locations)
- 3 different operating conditions (EC1, EC2, EC3)
- Total of 12 different scenarios
Implementation
https://github.com/alidi24/deep-learning-fault-diagnosis
License
This dataset is licensed under CC BY 4.0.
This means:
- ✅ You are free to share and adapt the data
- ✅ You must give appropriate credit
- ✅ You can use it for any purpose, including commercial use
Citation
In case using this dataset for your research, you are welcome to cite the paper:
@article{DIBAJ2023161,
title = {Fault detection of offshore wind turbine drivetrains in different environmental conditions through optimal selection of vibration measurements},
journal = {Renewable Energy},
volume = {203},
pages = {161-176},
year = {2023},
issn = {0960-1481},
doi = {https://doi.org/10.1016/j.renene.2022.12.049},
url = {https://www.sciencedirect.com/science/article/pii/S0960148122018341},
author = {Ali Dibaj and Zhen Gao and Amir R. Nejad},
keywords = {Offshore wind turbine, Drivetrain system, Fault detection, Optimal vibration measurements},
}
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