TY - GEN

T1 - Localized damage detection in wind turbine rotor blades using airborne acoustic emissions

AU - Lange, Alexander

AU - Hinrichs, Reemt

AU - Ostermann, Jörn

N1 - Funding Information: This research was funded by German Federal Ministry for Economic Affairs and Climate (BMWK) “Multivariate Damage Monitoring of Rotor Blades: Implementation and Analysis of the Effects of Repair Measures” (03EE2043C).

PY - 2023/3/30

Y1 - 2023/3/30

N2 - The human-induced climate change is one of the biggest threats for modern society. Wind energy turbines play a key role in the necessary transformation of the energy sector and their reliable, low-maintenance operation with short downtimes is therefore of particular interest. To this end, automated structural health monitoring (SHM) gained a lot of interest in research and economy. In this work, we propose an algorithm for damage detection in rotor blades using airborne acoustic emissions (AE). Our algorithm inherently uses a localization approach and is therefore not only able to detect a structural damage but also to estimate its position using time differences of arrival (TDoA) of airborne sound waves. Since we intend only an approximate localization of the impulsive damage sounds, we suggest a simple yet effective method based on cross-correlation of energy-envelope functions to estimate the TDoA. For the task of damage detection and localization, only two line-of-sight microphones are required, which makes this approach very economic for SHM. We evaluate our method on two large-scale fatigue tests conducted on a 34-meter and a 30-meter rotor blade under laboratory conditions. During the fatigue tests, we continuously recorded airborne sound signals with multiple microphones placed inside the rotor blades. With our proposed method, we are able to detect and correctly assign the two significant structural damages in both rotor blades to a two-meter-long rotor blade zone without having any false-positive alarms throughout more than 350 hours of continuous audio recordings. Airborne acoustic emissions therefore may be a promising alternative to other conventional monitoring solutions based on structure-borne sound, which usually require considerable denser sensor networks.

AB - The human-induced climate change is one of the biggest threats for modern society. Wind energy turbines play a key role in the necessary transformation of the energy sector and their reliable, low-maintenance operation with short downtimes is therefore of particular interest. To this end, automated structural health monitoring (SHM) gained a lot of interest in research and economy. In this work, we propose an algorithm for damage detection in rotor blades using airborne acoustic emissions (AE). Our algorithm inherently uses a localization approach and is therefore not only able to detect a structural damage but also to estimate its position using time differences of arrival (TDoA) of airborne sound waves. Since we intend only an approximate localization of the impulsive damage sounds, we suggest a simple yet effective method based on cross-correlation of energy-envelope functions to estimate the TDoA. For the task of damage detection and localization, only two line-of-sight microphones are required, which makes this approach very economic for SHM. We evaluate our method on two large-scale fatigue tests conducted on a 34-meter and a 30-meter rotor blade under laboratory conditions. During the fatigue tests, we continuously recorded airborne sound signals with multiple microphones placed inside the rotor blades. With our proposed method, we are able to detect and correctly assign the two significant structural damages in both rotor blades to a two-meter-long rotor blade zone without having any false-positive alarms throughout more than 350 hours of continuous audio recordings. Airborne acoustic emissions therefore may be a promising alternative to other conventional monitoring solutions based on structure-borne sound, which usually require considerable denser sensor networks.

KW - Acoustic Emission

KW - Damage Detection

KW - SHM

KW - Source Localization

KW - TDoA

UR - http://www.scopus.com/inward/record.url?scp=85159594607&partnerID=8YFLogxK

U2 - 10.21741/9781644902455-29

DO - 10.21741/9781644902455-29

M3 - Conference contribution

AN - SCOPUS:85159594607

SN - 9781644902448

T3 - Materials Research Proceedings

SP - 228

EP - 235

BT - Structural Health Monitoring

A2 - Rajic, Nik

A2 - Chiu, Wing Kong

A2 - Veidt, Martin

A2 - Mita, Akira

A2 - Takeda, N.

T2 - 9th Asia-Pacific Workshop on Structural Health Monitoring, 9APWSHM 2022

Y2 - 7 December 2022 through 9 December 2022

ER -