Wind Turbine Fault Detection through Autoencoder-Based Neural Networks

In the pursuit of sustainable and efficient energy solutions, wind turbines play a pivotal role. However, the operational efficiency of wind turbines can be significantly compromised by mechanical and structural faults. Early detection of such faults is crucial to prevent costly downtimes and extensive repairs. Traditional fault detection methods often fall short in accurately identifying complex fault patterns, particularly in the noisy environment characteristic of wind turbines. This research introduces a novel approach utilizing autoencoder-based neural networks to enhance the fault detection process in wind turbines. Autoencoders, a type of neural network, are well-suited for anomaly detection due to their ability to reconstruct input data and detect deviations from normal operational patterns. This paper details the development and implementation of a specialized autoencoder architecture designed to process and analyze monitoring data from wind turbines. The implications of this research are significant, offering a more reliable and efficient method for fault detection in wind turbines. This not only helps in reducing maintenance costs but also in extending the lifespan and operational efficiency of wind turbines. Future work will focus on refining the model's predictive capabilities, expanding the types of detectable faults, and integrating adaptive learning mechanisms to continually improve the model's accuracy based on new data. This study serves as a proof of concept for the broader application of autoencoder-based neural networks in industrial anomaly detection and sets the stage for future research in this promising area.