Blade Fault Diagnosis Based on Hybrid Physics and Domain Adaptation: A Case Study of Variable-Speed Marine Current Turbines

Le résumé fourni par la source

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.

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