A FAULT DETECTION MODEL FOR PREDICTIVE MAINTENANCE OF A WIND TURBINE BASED ON A HYBRID DEEP LEARNING APPROACH

In the context of Industry 4.0, predictive maintenance of wind turbines is a crucial issue for optimizing their performance.This article proposes a new hybrid deep learning approach, aligned with the principles of Industry 4.0, to improve the predictive detection of failures in wind turbines.By combining long short-term recurrent neural networks (LSTM), artificial neural networks (ANN), and genetic algorithms (GA), our hybrid model (GA-LSTM-ANN) effectively captures the temporal and nonlinear complexities of sensor data.Genetic algorithms automatically optimize the model configuration.This innovative approach offers a unique solution for predictive fault detection.Applied to a real dataset, our model has significantly outperformed traditional methods, achieving 96.32% precision, 95.91% accuracy, 96.45% F1-score, and 96.41% recall for wind turbine fault detection.These promising results open up new perspectives for optimizing the management of wind farms and contributing to the energy transition.