Damage mode identification and singular signal detection of composite wind turbine blade using acoustic emission. (1st January 2021)

• Record Type: Journal Article
• Authors: Xu, D.; Liu, P.F.; Chen, Z.P.
• Is Part Of: Composite structures. Volume 255(2021)
• Publication Date: 2021-01-01
• Subjects: Composite wind turbine blade -- Fatigue loads -- Acoustic emission (AE) -- Feature extraction -- Unsupervised learning -- Outlier detection; Composite construction -- Periodicals; Composites -- Périodiques; 624.18
• DOI: 10.1016/j.compstruct.2020.112954
• Languages: English
• ISSNs: 0263-8223

Abstract

Highlights: Robust damage mode identification and singular signal detection are achieved. The frequency characteristics of damage modes and noise sources are derived. The damage evolution behaviors of composite wind turbine blade are explored. Effects of two hyperparameters are considered to validate the method. Abstract: Some challenging issues emerge for the health monitoring of composite wind turbine blades under the intrinsic noise of fatigue loading, including damage mode identification and singular signal detection. This work performs health monitoring of a 59.5-m-long composite wind turbine blade under fatigue loads by acoustic emission (AE) technique. First, the original AE waveform is acquired after wave attenuation calibration and sensor array arrangement. Second, a waveform-based feature extraction method is developed based on the wavelet packet decomposition (WPD) to capture the information contained in original AE signals, which covers all features for reconstructed signals in the frequency domain. Without the requirements for signal preprocessing, clustering analysis is conducted for damage mode identification and singular signal detection based on the extracted features. Third, two hyperparameters, including the scatter number and the selection of wavelet basis function, are demonstrated to show no effect on the results, indicating the robustness of the method. This method is proved to be effective and feasible for health condition monitoring of the blade.