77 / 2025-05-14 19:50:31

Adversarial Discriminative Domain Adaptation for Drive Train of the Wind Turbines Fault Diagnosis Using Acoustic Emission

Wind Turbine, Fault Diagnosis, Acoustic Emission, Domain Adaptation

The Wind Turbines (WTs) experience gradual wear of gears and bearings under variable weather conditions.This paper presents a novel fault diagnosis method for the drive train of WTs based on Adversarial Discriminative Domain Adaptation (ADDA) and Acoustic Emission (AE). AE, serving as a non-destructive testing technology, is used to detect rub-impact faults in rotating machinery. ADDA, as a form of transfer learning (TL), takes knowledge from a source domain and applies it to a target domain, thereby improving the model's generalization capability. Experiments show that the proposed AE-ADDA method achieves excellent fault diagnosis results.It offers a novel approach to tackle the challenges of fault detection in wind turbine drive trains across varying operational conditions.