Cross-device target migration intelligent diagnosis method of wind power gearbox faults

Abstract

The current convolutional neural network (CNN) deep learning algorithm for wind power gearbox fault diagnosis requires a substantial amount of high-quality tagged data from testbeds to achieve accurate results. However, it may not perform well in practical engineering scenarios where there is limited fault tag data and incomplete fault information. To overcome this challenge, a novel approach utilizing cross-device target transfer for intelligent diagnosis of wind turbine gearbox faults is proposed. This method transforms the fault diagnosis of wind turbine gearboxes into an image recognition and classification task. Initially, the source domain gearbox testbed data is subjected to dimension transformation using the Gramian angular difference field technique, resulting in the generation of small sample two-dimensional images. This transformation simulates the limited fault tag data scenario encountered in practical engineering. Once the fault features have been augmented, the small sample source domain gearbox testbed data is fed into the IConvNeXt deep CNNs. These CNNs are interconnected and specifically designed for the field. They aim to extract and recognize fault features, thereby creating a pre-training model. The pre-training model is then transferred to the target domain wind power gearbox data for fault diagnosis experiments. The results of these experiments demonstrate the significant effectiveness of the proposed method in recognizing faults in small sample wind power gearboxes within the target domain. Overall, this method successfully achieves the goal of intelligent fault diagnosis through target transfer. It addresses the challenge of limited fault tag data and incomplete fault information, thereby enhancing the performance of CNNs in practical engineering scenarios.