Artificial Intelligence Tools for Wind Turbine Blade Monitoring

Electricity from wind turbines is popular and ecologically friendly. These gadgets must be reliable owing to the extensive usage of innovative materials. Researchers are creating efficient and cost-effective monitoring solutions for wind turbine blades, the most expensive part of a wind turbine. This study introduces a deep convolutional neural network-based wind turbine blade monitoring system based on medical auscultation. The system balances engineering dependability with economic efficiency. A lightweight architecture for monitoring wind turbine blades using edge computing and programmable logic controller signals is described in this study. Aerodynamic acoustic waves are collected and filtered by this technology. Our audio enhancement approaches combine self-adaptive mask targeting, multi-scale feature extraction, and deep neural networks to reduce wind turbine blade audio signal noise. Finally, we provide a unique technique to compress deep convolutional neural networks for peripheral computing devices with limited resources. Additionally, we optimise audio-generated spectrograms for wind turbine blade trouble diagnosis.