Transfer Learning Strategy for Fault Identification in Wind Turbine High-Speed Shaft Bearing with Limited Samples

Abstract

The application of deep learning algorithms for fault identification in wind turbine components is contingent on extensive data. Such data is often scarce, especially in the faulty category. While adversarial data augmentation helps, biases from the original data persist, and larger datasets strain computational resources. As a solution, experts are turning to transfer learning. Leveraging insights from related domains, transfer learning enables machine learning models to circumvent the exigency of training from scratch with extensive data. This study proposed a transfer learning strategy for fault identification in high-speed wind turbine shaft bearings. Two-dimensional matrices extracted from vibration signals sampled from the turbine bearings are employed to train ResNet50 and VGG16 convolutional neural network models with frozen weights based on transfer learning.  While both models performed well on the normal test samples, they showed differing robustness when evaluated with noise-induced test samples. Contrarily, the ResNet50 had an accuracy, F-score, and training time of 82.21%, 78.34%, and 26.3 s, respectively, while the VGG16 model had an accuracy and F-score of 95.55% and 95.35%, respectively, but trained for 46 s. The ResNet50 may have converged quickly due to “skip connection” in its architecture, typical of residual learning models. While the VGG16 is computationally intensive, its superior performance and resilience to noise make it suited for vibration-based defect detection in the high-speed shaft bearing, where severe background noise is prevalent.