Wind turbine blade health monitoring using acoustic beamforming techniques

Niezrecki, Christopher; Poozesh, Peyman; Aizawa, Kai; Heilmann, Gunnar

doi:10.1121/1.4877915

Cited by 14 publications

(6 citation statements)

References 3 publications

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“…Usually, microphones can map the received array to a plane after processing to show the location of the sound source distribution. Microphone arrays used to locate motion sources can effectively distinguish aerodynamic noise distributed at different locations of wind turbine blades and are promising for wind turbine blade damage detection [134][135][136][137]. Aizawa and Poozesh et al [138,139] investigated phased array beam formation and two beam formation algorithms (CLEAN and CLPSR) for wind turbine blade damage detection.…”

Section: Wind Turbine Blade Damage Detection Based On Beamforming or ...mentioning

confidence: 99%

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Ding

Chao

Zhang

2023

Sensors

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Monitoring and maintaining the health of wind turbine blades has long been one of the challenges facing the global wind energy industry. Detecting damage to a wind turbine blade is important for planning blade repair, avoiding aggravated blade damage, and extending the sustainability of blade operation. This paper firstly introduces the existing wind turbine blade detection methods and reviews the research progress and trends of monitoring of wind turbine composite blades based on acoustic signals. Compared with other blade damage detection technologies, acoustic emission (AE) signal detection technology has the advantage of time lead. It presents the potential to detect leaf damage by detecting the presence of cracks and growth failures and can also be used to determine the location of leaf damage sources. The detection technology based on the blade aerodynamic noise signal has the potential of blade damage detection, as well as the advantages of convenient sensor installation and real-time and remote signal acquisition. Therefore, this paper focuses on the review and analysis of wind power blade structural integrity detection and damage source location technology based on acoustic signals, as well as the automatic detection and classification method of wind power blade failure mechanisms combined with machine learning algorithm. In addition to providing a reference for understanding wind power health detection methods based on AE signals and aerodynamic noise signals, this paper also points out the development trend and prospects of blade damage detection technology. It has important reference value for the practical application of non-destructive, remote, and real-time monitoring of wind power blades.

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Section: Wind Turbine Blade Damage Detection Based On Beamforming or ...mentioning

confidence: 99%

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Ding

Chao

Zhang

2023

Sensors

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“…One of them is acoustic emission testing, in which microphones are placed on the structures to capture frequencies generated by internal stress to identify structural defects. Another is elastic wave propagation, which involves sending controlled sound waves by piezoelectric transducers through materials to evaluate their structural integrity [12][13][14]. In response to the need for this, our suggested research puts itself at the forefront of finding better ways to monitor WTBs with the help of LDVs as an NDT [15].…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Wind Turbine Blade Crack Detection Using Laser Doppler Vibrometers

Zabihi,

Aghdasi,

Ellouzi

et al. 2024

Energies

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In response to the growing global demand for both energy and a clean environment, there has been an unprecedented rise in the utilization of renewable energy. Wind energy plays a crucial role in striving for carbon neutrality due to its eco-friendly characteristics. Despite its significance, wind energy infrastructure is susceptible to damage from various factors including wind or sea waves, rapidly changing environmental conditions, delamination, crack formation, and structural deterioration over time. This research focuses on investigating non-destructive testing (NDT) of wind turbine blades (WTBs) using approaches based on the vibration of the structures. To this end, WTBs are first made from glass fiber-reinforcement polymer (GFRP) using composite molding techniques, and then a short pulse is generated in the structure by a piezoelectric actuator made from lead zirconate titanate (PZT-5H) to generate guided waves. A numerical approach is presented based on solving the elastic time-harmonic wave equations, and a laser Doppler vibrometer (LDV) is utilized to collect the vibrational data in a remote manner, thereby facilitating the crack detection of WTBs. Subsequently, the wave propagation characteristics of intact and damaged structures are analyzed using the Hilbert–Huang transformation (HHT) and fast Fourier transformation (FFT). The results reveal noteworthy distinctions in damaged structures, where the frequency domain exhibits additional components beyond those identified by FFT, and the time domain displays irregularities in proximity to the crack region, as detected by HHT. The results suggest a feasible approach to detecting potential cracks of WTBs in a non-contact and reliable way.

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“…Ultrasound-based inspection is one of the most widely used blade inspection techniques in industry and has been successfully demonstrated in the literature [10][11][12]. Acoustic beamforming is a versatile but costly technique that has been demonstrated to succeed under certain conditions [13,14]. Acoustic signatures taken from the structure, before and after damage, may allow for detection and differentiation of fault existence and can also be used as a blade inspection method [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Blade Damage Detection Using Supervised Machine Learning Algorithms

Regan

Beale

İnalpolat

2017

Journal of Vibration and Acoustics

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Wind turbine blades undergo high operational loads, experience variable environmental conditions, and are susceptible to failure due to defects, fatigue, and weather-induced damage. These large-scale composite structures are fundamentally enclosed acoustic cavities and currently have limited, if any, structural health monitoring (SHM) in place. A novel acoustics-based structural sensing and health monitoring technique is developed, requiring efficient algorithms for operational damage detection of cavity structures. This paper describes the selection of a set of statistical features for acoustics-based damage detection of enclosed cavities, such as wind turbine blades, as well as a systematic approach used in the identification of competent machine learning algorithms. Logistic regression (LR) and support vector machine (SVM) methods are identified and used with optimal feature selection for decision-making via binary classification algorithms. A laboratory-scale wind turbine with hollow composite blades was built for damage detection studies. This test rig allows for testing of stationary or rotating blades, of which time and frequency domain information can be collected to establish baseline characteristics. The test rig can then be used to observe any deviations from the baseline characteristics. An external microphone attached to the tower will be utilized to monitor blade health while blades are internally ensonified by wireless speakers. An initial test campaign with healthy and damaged blade specimens is carried out to arrive at several conclusions on the detectability and feature extraction capabilities required for damage detection.

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Wind turbine blade health monitoring using acoustic beamforming techniques

Cited by 14 publications

References 3 publications

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Non-Contact Wind Turbine Blade Crack Detection Using Laser Doppler Vibrometers

Wind Turbine Blade Damage Detection Using Supervised Machine Learning Algorithms

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