Acoustic Emission (AE) Source Monitoring in Composite Wind Turbine Blades using Narrow Frequency Bands and Machine Learning

Abstract

Wind turbines (WT) need to perform well to meet the ever-growing demand of green energy. However, the performance of a wind turbine can be jeopardized due to occurrence of damage within its components. Acoustic emission (AE) is highly sensitive to occurrence of damage and can be used for damage identification. However, the localization of an AE source is challenging using a time domain method for structures that involve material anisotropy and complex geometry (e.g., curved surfaces and variable thickness). With this view, this work presents an unsupervised framework that operates on narrow frequency bands (NFBs) for artificial AE source determination in composite wind turbine blades. Unlike the conventional time-domain methods that require a network of sensors and the time of arrival information, the new method requires only one sensor and the information contained in the reduced frequency bands within each AE signal to discern its source. The unsupervised framework is found to be highly efficient in finding pattern in the multi-dimensional frequency domain dataset extracted from AE signals and can easily cluster the AE signals as per their zone of occurrence in a WT blade. This method can be easily implemented in real and complex structures as it requires only one sensor. Due to the very high degree of accuracy, the method can be applied to real complex systems and structures where time domain methods are not feasible. The method can also be included in a digital twin model for accurate prediction of an AE source.