Blade Fault Diagnosis Based on Hybrid Physics and Domain Adaptation: A
Case Study of Variable-Speed Marine Current Turbines
As a machine learning approach, domain adaptation methods are widely
applied in cross-scenario fault diagnosis. However, the target domain
may need more annotated data, posing challenges to the performance of
domain adaptation methods. This paper proposes a fault diagnosis method
based on hybrid physics and domain adaptation (HPDA) with its
application to marine current turbines (MCTs) scenarios. Specifically,
this method first establishes a rotational feature alignment model based
on physical variables. Then, it aligns the feature of the target domain
data with physical parameters. Finally, an augmented domain adversarial
model is trained using pre-alignment samples. Data from MCT prototypes
are collected to validate the effectiveness of the proposed method.
Experimental results demonstrate the proposed method’s superior
stability and data transferability compared with the state-of-the-art.