File size: 3,221 Bytes
5a024f2 ab7be4d 646cadb ab7be4d 646cadb a3079cb 646cadb a3079cb 646cadb a3079cb 646cadb a3079cb 646cadb df233b6 5a024f2 ab7be4d df233b6 ab7be4d e7ee7bb ab7be4d df233b6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
---
dataset: Wind-Turbine-5MW-Drivetrain-Bearing-Damage-Dataset
language:
- en
tags:
- bearing-fault
- drivetrain
- condition-monitoring
- vibration-signal
- wind-turbine
- fault-diagnosis
license: cc-by-4.0
dataset_info:
features:
- name: signal_a1a
sequence:
sequence: float32
- name: signal_a3a
sequence:
sequence: float32
- name: signal_length
dtype: int32
- name: label
dtype:
class_label:
names:
'0': healthy
'1': main_bearing
'2': high_speed_bearing
'3': low_speed_planet_bearing
- name: fault_condition
dtype: string
- name: environmental_condition
dtype: string
- name: sampling_frequency
dtype: float32
- name: wind_speed
dtype: float32
splits:
- name: train
num_bytes: 69120747
num_examples: 12
download_size: 70324111
dataset_size: 69120747
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
pretty_name: 5MW offshore wind turbine drivetrain bearing damage dataset
---
# 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](https://doi.org/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](https://doi.org/10.5281/zenodo.7674842)
### Features:
- **`signal_a1a`**: Vibration time waveform from sensor A1A
- **`signal_a3a`**: Vibration time waveform from sensor A3A
- **`signal_length`**: Length of the signals
- **`label`**: 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 Hz
- **`wind_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:
```bibtex
@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},
}
``` |