Experimental Dynamic Characterization of Both Surfaces of Structures using 3D Scanning Laser Doppler Vibrometer

Chen, Yuanchang; Mendoza, Alejandra S. Escalera; Griffith, D. Todd

doi:10.1007/s40799-022-00604-2

Cited by 9 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scanning laser Doppler vibrometer (SLDV) is widely used in damage detection-based vibration measurements. 29 Chen et al 30 propose a new method of modal shapes testing in WT blade using a high spatial resolution 3D SLDV. Liu et al 31 propose the algorithm which is applied to an SLDV to improve the accuracy of modal parameter measurements.…”

Section: Damage Localization Based On Svdmentioning

confidence: 99%

Method using singular value decomposition and whale optimization algorithm to quantitatively detect multiple damages in turbine blades

Jiang

Xiang

2023

Structural Health Monitoring

View full text Add to dashboard Cite

Renewable energy has increased in recent years with a consequential increase in equipment maintenance. Maintenance costs can be reduced by structural health monitoring techniques especially for wind turbine (WT) blade damages. However, the majority are not suitable for on-line measurements and quantitative detections. A quantitative damage detection method is developed to identify multiple damages in a WT blade under in-service operation conditions. Firstly, singular value decomposition is applied to reveal singular information in the operating deflection shape (ODS), which can be treated as damage locations. Secondly, whale optimization algorithm is utilized for a damage severity decision about the natural frequency database between damage severities and natural frequencies, which are constructed by finite element method (FEM) simulations on the detected damage locations in the WT blade. The procedure is applied to FEM numerical simulations of a single WT blade with two and three damages. By adding a certain noise to the simulation dataset, the robustness of the present method is validated. Furthermore, the laser scanning vibrometer is employed to test the ODS as well as natural frequencies of WT blades to testify the performance of the multiple damage detection method. Results show that the present method is effective for the detection of multi-damage in WT blades with a certain noise robustness.

show abstract

Section: Damage Localization Based On Svdmentioning

confidence: 99%

Method using singular value decomposition and whale optimization algorithm to quantitatively detect multiple damages in turbine blades

Jiang

Xiang

2023

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…Then, the results were validated by finite element model and strain gauge measurement. For more details, one can see [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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

Zabihi,

Aghdasi,

Ellouzi

et al. 2024

Energies

View full text Add to dashboard Cite

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.

show abstract

“…For instance, LDV is a suitable method for blade monitoring since it allows measurements of its surface vibration [ 19 ]. Chen et al [ 20 ] proposed an experimental modal testing the technology of WTBs using 3D LDV. The two surfaces of the blade are scanned in a non-contact fashion to characterize the mode shapes of the blade.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection for Rotating Blades Using Digital Image Correlation with an AC-SURF Matching Algorithm

Liu

2022

Sensors

View full text Add to dashboard Cite

The motion information of blades is a key reflection of the operation state of an entire wind turbine unit. However, the special structure and operation characteristics of rotating blades have become critical obstacles for existing contact vibration monitoring technology. Digital image correlation performs powerfully in non-contact, full-field measurements, and has increasingly become a popular method for solving the problem of rotating blade monitoring. Aiming at the problem of large-scale rotation matching for blades, this paper proposes a modified speeded-up robust features (SURF)-enhanced digital image correlation algorithm to extract the full-field deformation of blades. Combining an angle compensation (AC) strategy, the AC-SURF algorithm is developed to estimate the rotation angle. Then, an iterative process is presented to calculate the accurate rotation displacement. Subsequently, with reference to the initial state of rotation, the relative strain distribution caused by flaws is determined. Finally, the sensitivity of the strain is validated by comparing the three damage indicators including unbalanced rotational displacement, frequency change, and surface strain field. The performance of the proposed algorithm is verified by laboratory tests of blade damage detection and wind turbine model deformation monitoring. The study demonstrated that the proposed method provides an effective and robust solution for the operation status monitoring and damage detection of wind turbine blades. Furthermore, the strain-based damage detection algorithm is more advantageous in identifying cracks on rotating blades than one based on fluctuated displacement or frequency change.

show abstract

Experimental Dynamic Characterization of Both Surfaces of Structures using 3D Scanning Laser Doppler Vibrometer

Cited by 9 publications

References 27 publications

Method using singular value decomposition and whale optimization algorithm to quantitatively detect multiple damages in turbine blades

Method using singular value decomposition and whale optimization algorithm to quantitatively detect multiple damages in turbine blades

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

Damage Detection for Rotating Blades Using Digital Image Correlation with an AC-SURF Matching Algorithm

Contact Info

Product

Resources

About