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research-article

WIND TURBINE BLADE DAMAGE DETECTION USING SUPERVISED MACHINE LEARNING ALGORITHMS

[+] Author and Article Information
Taylor Regan

University of Massachusetts Lowell Lowell, MA 01854
tmregan11235@gmail.com

Christopher Beale

University of Massachusetts Lowell Lowell, MA 01854
cbeale5150@gmail.com

Murat Inalpolat

University of Massachusetts Lowell 1 University Avenue, Lowell, MA 01854
Murat_Inalpolat@uml.edu

1Corresponding author.

ASME doi:10.1115/1.4036951 History: Received June 10, 2016; Revised May 26, 2017

Abstract

Wind turbine blades undergo high operational loads, experience variable environmental conditions, and are susceptible to failure due to defects, fatigue, and weather induced damage. These large-scale composite structures are fundamentally enclosed acoustic cavities and currently have limited, if any, structural health monitoring in place. A novel acoustics-based structural sensing and health monitoring technique is developed, requiring efficient algorithms for operational damage detection of cavity structures. This paper describes the selection of a set of statistical features for acoustics-based damage detection of enclosed cavities, such as wind turbine blades, as well as a systematic approach used in the identification of competent machine learning algorithms. Logistic regression (LR) and support vector machine (SVM) methods are identified and used with optimal feature selection for decision making via binary classification algorithms. A laboratory-scale wind turbine with hollow composite blades was built for damage detection studies. This test rig allows for testing of stationary or rotating blades, of which time and frequency domain information can be collected to establish baseline characteristics. The test rig can then be used to observe any deviations from the baseline characteristics. An external microphone attached to the tower will be utilized to monitor blade health while blades are internally ensonified by wireless speakers. An initial test campaign with healthy and damaged blade specimens is carried out to arrive at several conclusions on the detectability and feature extraction capabilities required for damage detection.

Copyright (c) 2017 by ASME
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