A distance-aware approach for reliable out-of-distribution detection of wind turbine gearbox fault diagnosis - Zhou, Jingxin; Zhao, Yao (Frontiers in Energy Research, 2024)
Fault diagnosis of wind turbine gearbox is essential to ensure operational efficiency and prevent costly downtime. However, conventional deep learning models often struggle with domain shift, where the distribution of testing data differs from that of training data. This issue is more pronounced with out-of-distribution inputs—data outside the conditions the model was trained on. These challenges can lead to unreliable diagnostic results and potentially hazardous situations. To address this, we introduce Spectral Normalization and Gaussian Process methods into Res2Net framework to enhance its ability to detect out-of-distribution data. Spectral Normalization and Gaussian Process improve the model’s ability to assess the distance between test and training data. This model can handle out-of-distribution data due to both epistemic and aleatory uncertainty. The experiment collected raw vibration signals from gearbox under varied conditions. Unknown faults simulated epistemic uncertainty, while noisy samples resulted in aleatory uncertainty. These signals were converted into images using the Gramian Angular Difference Field transformation. The resulting images were then fed into the Res2Net model, enhanced with Spectral Normalization and Gaussian Process. The model outputs include classification results and corresponding uncertainty values based on distance awareness. With quantified uncertainty values, the model can reflect the trustworthiness of the diagnostic results. By comparing these uncertainty values with predefined thresholds, it is possible to distinguish whether the data are out-of-distribution or not. Experiments have proven the superiority of the Distance-Aware Res2Net in out-of-distribution detection and fault diagnosis.