An unsupervised data-driven approach for wind turbine blade damage detection under passive acoustics-based excitation. Issue 4 (August 2022)

• Record Type: Journal Article
• Authors: Solimine, Jaclyn; Inalpolat, Murat
• Is Part Of: Wind engineering. Volume 46:Issue 4(2022)
• Page Start: 1311
• Page End: 1330
• Publication Date: 2022-08
• Subjects: Data-driven -- acoustics -- structural health monitoring -- wind turbine blades -- unsupervised; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X221080470
• Languages: English
• ISSNs: 0309-524X

Abstract

Existing passive acoustics-based techniques for wind turbine blade damage detection lack the robustness and adaptability necessary for an operational implementation due to their physics- and model-based dependency. In contrast, this study develops an entirely unsupervised, data-driven damage detection technique. The novelty of the technique lies in (i) the development and comparison of spectral and cepstral-domain features for the robust characterization of the cavity-internal acoustics, (ii) the use of autoencoder networks to reduce the effects of non-stationary acoustic excitation, and (iii) the exclusion of labeled or damage-case data in the training set. The technique was successfully demonstrated on a wind turbine blade section inflicted with damage of various sizes, types, and locations, and subjected to airflow-induced passive acoustic excitation provided by a wind tunnel. Damage detection accuracy up to 99.82% was achieved for some damage types.