Reliable Wind Turbine Blade Performance Monitoring System Using Aerodynamic Audio Signals and Deep Learning Approaches

Abstract

Wind turbines have emerged as a prominent and environmentally friendly energy generation solution. However, with the widespread use of new materials, ensuring the reliability of these devices has become as a critical issue. Developing efficient and cost-effective monitoring methods for the wind turbine's blades (WTBs), the most expensive components of wind turbine, has become a focal point of research. In this article, we present a novel monitoring system for WTBs that employs a deep convolutional neural network approach based on the medical auscultatory method. The system is designed to balance economic efficiency and engineering reliability. First, we proposed a lightweight WTBs monitoring framework based on edge computing that leverages the signals from the programmable logic controller output of wind turbine to enable efficient collection of relevant aerodynamic audio signals while filtering out irrelevant data. Second, we present a set of audio enhancement algorithms that employ multiscale feature extraction, self-adaptive mask targeting, and deep neural networks to reduce noise in the audio signals generated by WTBs. Third, we introduce a new approach for compressing deep convolution neural networks that makes them suitable for resource-constrained edge computing devices and efficiently utilizes audio-generated spectrograms to diagnose faults in WTBs.