Acoustic Emission Analysis for Wind Turbine Blade Bearing Fault Detection Using Sparse Augmented Lagrangian Algorithm

2020 ◽  
Author(s):  
Zepeng Liu ◽  
Long Zhang
Keyword(s):  
Acoustic Emission ◽  
Fault Detection ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Augmented Lagrangian ◽  
Wind Turbine Blade ◽  
Emission Analysis ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Acoustic Emission Analysis

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

2019 ◽  
Vol 19 (4) ◽  
pp. 1092-1103 ◽  
Author(s):  
Pengfei Liu ◽  
Dong Xu ◽  
Jingguo Li ◽  
Zhiping Chen ◽  
Shuaibang Wang ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Mode Identification ◽  
Emission Signal ◽  
Composite Blade ◽  
Acoustic Emission Sensors ◽  
Safety Precaution ◽  
Fatigue Loads

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.

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