Structural health monitoring of carbon fiber reinforced polymer composite laminates for offshore wind turbine blades based on dual maximum correlation coefficient method

Kai Luo, Liang Chen and Wei Liang

Renewable Energy, 2022, vol. 201, issue P1, 1163-1175

Abstract: Structural health monitoring (SHM) of offshore wind turbine blades (OWTBs) is critical to the safety of wind turbines, which can avoid catastrophic failures and reduce offshore wind turbine operation and maintenance costs. Combined with the damage imaging algorithm based on the reconstruction algorithm for the probabilistic inspection of defects (RAPID), a Lamb wave baseline-free detection method based on maximum similarity of forward-path is proposed to detect the damage of OWTB carbon fiber-reinforced polymer composite laminates. Damage can introduce nonlinearity into the structure, affecting the correlation of the response signal in the forward path with the original input signal. A new method for calculating the baseline-free damage index (DI) based on the dual maximum correlation coefficient method (DMCCM) is proposed by analyzing the maximum correlation coefficient between the forward path response signal and the original excitation signal. The proposed DMCCM compensates the DI by twice comparing the local maximum correlation coefficients and reducing the errors caused by the interference of the reflected wave to the direct wave and the offset of the peak point when intercepting the response signal. Experimental studies are performed using OWTB composite laminates to validate the proposed method further. The detection results of DMCCM are more reliable, accurate, and require less time than existing methods, which is a very promising baseline-free method for visual SHM of OWTB composite laminates.

Keywords: (OWTB) Offshore wind turbine blade; Structural health monitoring; Composite laminates; (DMCCM) Dual maximum correlation coefficient method; Baseline-free; Lamb wave (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:201:y:2022:i:p1:p:1163-1175

DOI: 10.1016/j.renene.2022.11.063

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