An Adversarial Single-Domain Generalization Network for Fault Diagnosis of Wind Turbine Gearboxes

Abstract

In deep learning-based fault diagnosis of the wind turbine gearbox, a commonly faced challenge is the domain shift caused by differing operational conditions. Traditional domain adaptation methods aim to learn transferable features from the source domain and apply them to the target data. However, such methods still require access to target domain data during the training process, which limits their applicability in real-time fault diagnosis. To address this issue, we introduce an adversarial single-domain generalization network (ASDGN). It relies solely on data from a single length of data acquisition in wind turbine fault diagnosis. This novel approach introduces a more flexible and efficient solution to the field of real-time fault diagnosis for wind turbines.

Research on Abrasive Particle Target Detection and Feature Extraction for Marine Lubricating Oil

“…During the operation of the ship's hydraulic system, some contaminants will be mixed into the hydraulic oil, and when their content exceeds a certain limit, thus affects the normal operation of the whole system. Therefore, in order to avoid the failure of the ship's hydraulic equipment, it is necessary to detect the solid metal particle contaminants in the hydraulic oil [3,4].…”