End-to-End Deep Learning Approach for Wind Turbine Bearing Fault Detection From Acoustic Data - Oliveira, Gabriel Luiz Barros De; Alves, Lucas; Rocha, Cleilton L.; Lima, João Pedro B.; Mesquita, Paulo; Trajano, Alex (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2025: Proceedings of the 31st Brazilian Symposium on Multimedia and the WebDO - 10.5753/webmedia.2025.15198, 2025)
Wind energy generation through wind turbines has become increasingly attractive as a clean and renewable energy source. Effective maintenance of wind turbines is crucial, as failure can result in significant economic losses and damage to equipment due to unplanned downtime. Nevertheless, ensuring effective maintenance remains challenging because these systems usually operate in severe and remote environments. Considering that rolling bearings are among the most necessary mechanical components in wind turbines, accurately detecting faults in these bearings is important for ensuring the regular and reliable operation of the equipment. This paper proposes a deep learning-based monitoring architecture that utilizes acoustic signals emitted from rolling bearings to detect faults in wind turbines. Using real-world data collected via microphones and a Raspberry Pi system, we constructed a structured and manually annotated dataset. A convolutional neural network (CNN) model was trained on mel-spectrogram representations to distinguish between healthy and faulty operational states. The system achieved promising performance, with 83.62% accuracy, 87.40% F1-score, 95% AUC-ROC, and 98% precision-recall AUC. The proposed end-to-end pipeline integrates data acquisition, pre-processing, classification, and confidence-based decision thresholds, making it suitable for deployment in operational monitoring scenarios. These results demonstrate the viability of audio-based fault detection as a scalable and non-invasive solution for predictive maintenance in wind energy systems.