Data for the Paper: Audio Signal-Based Defect Detection for Wind Turbine Rotor Blades Using an Autoencoder
| dc.contributor.author | Meding, Finn | |
| dc.contributor.author | Feldmann, Robert | |
| dc.date.accessioned | 2026-05-26T15:19:46Z | |
| dc.date.created | 2025 | |
| dc.date.issued | 2026-05-26 | |
| dc.description | This folder contains all the data used for the paper: R. Feldmann, F. Meding, and T. Melz, "Audio Signal-Based Defect Detection for Wind Turbine Rotor Blades Using an Autoencoder," in Proceedings of ISMA2026 - International Conference on Noise and Vibration Engineering, Leuven, Belgium, 2026. This dataset contains: acoustic measurement data for the training of neural networks to detect defects in structures like wind turbine rotor blades. Recorded in 2026 in the semi-anechoic chamber at the Technical University of Darmstadt (SAM), the data comprises individual acoustic emission signals (.wav) obtained via local acoustic resonance spectroscopy across a 48-point grid on eight glass fibre reinforced polymer (GRP) test plates (360 mm * 280 mm). While plates 1–4 represent fully intact structures subsequently subjected to impact damage measurements, plates 5–8 feature specifically introduced delamination defects. The dataset includes: both raw emissions and a pre-split structure (70% training, 15% validation, 15% test) tailored for neural network training. Augmented subsets feature environmental background noise (drone, traffic, birds, wind, wind turbine) augmented at Signal-to-Noise Ratios (SNR) from 9 dB to 3 dB. A standardized file-naming convention directly embeds structural parameters, mounting types, spatial coordinates, noise types, and SNR values. Details and the use of the dataset can be found in the readme. | |
| dc.description.version | 1.0 | |
| dc.identifier.uri | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/5152 | |
| dc.identifier.uri | https://doi.org/10.48328/tudatalib-2241 | |
| dc.rights.license | CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0) | |
| dc.subject | defect recognition | |
| dc.subject | glass-fiber-reinforced-plastic (GRP) | |
| dc.subject | local acoustic resonance spectroscopy (LARS) | |
| dc.subject | neural networks | |
| dc.subject.classification | 4.12-03 | |
| dc.subject.ddc | 620 | |
| dc.title | Data for the Paper: Audio Signal-Based Defect Detection for Wind Turbine Rotor Blades Using an Autoencoder | |
| dc.type | Sound | |
| dcterms.accessRights | openAccess | |
| person.identifier.orcid | 0009-0005-2157-6572 | |
| person.identifier.orcid | 0000-0001-6932-2401 | |
| tuda.agreements | true | |
| tuda.unit | TUDa |
Files
Original bundle
1 - 13 of 13
| Name | Description | Size | Format | |
|---|---|---|---|---|
| test_data.zip | 424.44 MB | ZIP-Archivdateien | ||
| validation_data.zip | 70 MB | ZIP-Archivdateien | ||
| unaugmented_data.zip | 50.93 MB | ZIP-Archivdateien | ||
| noise_data.zip | 1.96 MB | ZIP-Archivdateien | ||
| raw_measurement_data.zip | 560.26 MB | ZIP-Archivdateien | ||
| README.md | 1.85 KB | Unknown data format | ||
| README_noise_data.md | 1.14 KB | Unknown data format | ||
| README_training_data.md | 1.18 KB | Unknown data format | ||
| README_raw_measurement_data.md | 1.71 KB | Unknown data format | ||
| README_test_data.md | 1.31 KB | Unknown data format | ||
| README_unaugmented_data.md | 1.08 KB | Unknown data format | ||
| README_validation_data.md | 1.18 KB | Unknown data format | ||
| training_data.zip | 98.37 MB | ZIP-Archivdateien |