Artificial Neural Network-Based Diagnosis of Wind Turbine Blade Faults Using Vibration Analysis at Constant Operational Speed
Abstract
Wind turbine blade faults, such as surface erosion, cracks, mass imbalance, and twist deformation, significantly compromise operational efficiency and reliability, thereby increasing maintenance costs. This research presents an artificial neural network (ANN)-based diagnostic approach for identifying five distinct fault states in wind turbine blades using vibration signal data collected at a constant operational speed of 1.3 m/s. The dataset, which encapsulates real-world vibration responses under varying fault conditions, was analyzed to extract amplitude features for classification. A balanced dataset of 500 samples per class was used to ensure robust training and evaluation. The ANN model achieved highly reliable performance, with classification accuracies (CA) of 96.21% (crack), 97.12% (erosion), 95.47% (healthy), 96.04% (twist deformation), and 94.38% (mass imbalance). Corresponding F1-scores were 95.32%, 96.51%, 94.45%, 95.18%, and 93.36%, respectively. These results confirm the model's effectiveness in distinguishing between common wind turbine blade faults and healthy conditions. This study demonstrates the potential of ANN-based systems for intelligent fault detection in wind energy systems, aiding in the advancement of condition-based maintenance and operational safety.