A Pattern Recognition Approach to Acoustic Emission Data Originating from Fatigue of Wind Turbine Blades

Sensors

卷:17

Cristinel Mares¹  Jialin Tang²  Slim Soua²  Tat-Hean Gan² 

[1] College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK;

[2] Integrity Management Group, TWI Ltd., Cambridge CB21 6AL, UK;

关键词: acoustic emission;    pattern recognition;    fatigue;    wind turbine blade;    composite;    piezoelectric sensors;   

DOI  :  10.3390/s17112507

来源: DOAJ

【 摘 要 】

The identification of particular types of damage in wind turbine blades using acoustic emission (AE) techniques is a significant emerging field. In this work, a 45.7-m turbine blade was subjected to flap-wise fatigue loading for 21 days, during which AE was measured by internally mounted piezoelectric sensors. This paper focuses on using unsupervised pattern recognition methods to characterize different AE activities corresponding to different fracture mechanisms. A sequential feature selection method based on a k-means clustering algorithm is used to achieve a fine classification accuracy. The visualization of clusters in peak frequency−frequency centroid features is used to correlate the clustering results with failure modes. The positions of these clusters in time domain features, average frequency−MARSE, and average frequency−peak amplitude are also presented in this paper (where MARSE represents the Measured Area under Rectified Signal Envelope). The results show that these parameters are representative for the classification of the failure modes.

【 授权许可】

Unknown