Wind turbine blades undergo high operational loads, experience variable environmental conditions, and are susceptible to failures due to defects, fatigue, and weather induced damage. These large-scale composite structures are essentially enclosed acoustic cavities and currently have limited, if any, structural health monitoring in practice. 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 a systematic approach used in the identification of a competent machine learning algorithm as well as a set of statistical features for acoustics-based damage detection of enclosed cavities, such as wind turbine blades. Logistic regression (LR) and support vector machine (SVM) methods are identified and used with optimal feature selection for decision making using binary classification. 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 (each fit with an internally located speaker and microphone), 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 also be utilized to monitor blade damage while blades are internally ensonified by wireless speakers. An initial test campaign with healthy and damaged blade specimens is carried out to arrive at certain conclusions on the detectability and feature extraction capabilities required for damage detection.
Alarms-related wind turbine fault detection based on kernel support vector machines
