Damage mode identification of composite wind turbine blade under accelerated fatigue loads using acoustic emission and machine learning

Author:

Liu Pengfei1ORCID,Xu Dong2,Li Jingguo2,Chen Zhiping2,Wang Shuaibang2,Leng Jianxing1,Zhu Ronghua1,Jiao Lei1,Liu Weisheng3,Li Zhongxiang3

Affiliation:

1. Ocean College, Zhejiang University, Zhoushan, China

2. Institute of Chemical Machinery and Process Equipment, School of Energy Engineering, Zhejiang University, Hangzhou, China

3. Key Laboratory for Design and Manufacturing of Offshore Wind Power Blade of Jiangsu Province, Lianyungang Zhongfu Lianzhong Composites Group Co., Ltd., Lianyungang, China

Abstract

This article studies experimentally the damage behaviors of a 59.5-m-long composite wind turbine blade under accelerated fatigue loads using acoustic emission technique. First, the spectral analysis using the fast Fourier transform is used to study the components of acoustic emission signals. Then, three important objectives including the attenuation behaviors of acoustic emission waves, the arrangement of sensors as well as the detection and positioning of defect sources in the composite blade by developing the time-difference method among different acoustic emission sensors are successfully reached. Furthermore, the clustering analysis using the bisecting K-means method is performed to identify different damage modes for acoustic emission signal sources. This work provides a theoretical and technique support for safety precaution and maintaining of in-service blades.

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Funding of China

The project of Zhongfulianzhong Composite Group Limited Company

Publisher

SAGE Publications

Subject

Mechanical Engineering,Biophysics

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