Wind Turbine Blades Fault Diagnosis Based on Vibration Signals Utilizing Convolutional Neural Network

Wind energy is a key contributor to renewable energy production, with wind turbines playing a critical role in its generation. However, the operational efficiency of wind turbines is often compromised by blade faults, leading to reduced performance, expensive maintenance, and potential safety hazards. Traditional methods of diagnosing faults in wind turbine blades are limited in both accuracy and efficiency. This study proposes a novel fault diagnosis approach using Convolutional Neural Networks (CNNs), a powerful deep learning technique for data analysis. The dataset comprises four sets of vibration signals representing both healthy and faulty wind turbine blades, covering fault conditions such as blade cracks, surface degradation, imbalance, and blade twist. Our proposed CNN-based method aims to accurately detect and classify these faults, enabling proactive maintenance and improving the overall reliability and efficiency of wind energy systems.