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Sensors 2017, 17(11), 2507; doi:10.3390/s17112507

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

1
Integrity Management Group, TWI Ltd., Cambridge CB21 6AL, UK
2
College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK
*
Author to whom correspondence should be addressed.
Received: 19 September 2017 / Revised: 20 October 2017 / Accepted: 25 October 2017 / Published: 1 November 2017
(This article belongs to the Special Issue Sensor Technologies for Health Monitoring of Composite Structures)
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Abstract

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. View Full-Text
Keywords: acoustic emission; pattern recognition; fatigue; wind turbine blade; composite; piezoelectric sensors acoustic emission; pattern recognition; fatigue; wind turbine blade; composite; piezoelectric sensors
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Tang, J.; Soua, S.; Mares, C.; Gan, T.-H. A Pattern Recognition Approach to Acoustic Emission Data Originating from Fatigue of Wind Turbine Blades. Sensors 2017, 17, 2507.

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