<p>Accurate fault diagnosis of complex energy systems, such as wind turbines, is essential to avoid catastrophic accidents and ensure a stable power source. However, accurate fault diagnoses under dynamic operating conditions and various failure mechanisms are still major challenges for wind turbines nowadays. Here we present a CapsNet-based deep learning scheme for data-driven fault diagnosis used in a digital twin of wind turbine gearboxes. The CapsNet model can extract&nbsp;the multi-dimensional features and rich spatial information from the gearbox monitoring data through a unique structure called the capsule network. Through the dynamic routing algorithm between capsules, the network structure and parameters of the CapsNet model can be adjusted effectively, so as to realize an accurate and robust classification of the operational conditions of a wind turbine gearbox, including both single and coupling faults. Two wind turbine gearbox datasets are used to verify the performance of the CapsNet model. The experimental accuracy of this proposed method is up to 98.3%, which proves the accuracy of CapsNet model in the case study. The experimental results show that, compared with state-of-the-art fault diagnosis methods reported in the literature, the CapsNet model has a competitive advantage, especially in the ability to diagnose coupling faults. In addition, the method can automatically extract features from the original monitoring data, and does not rely on expert experience or signal processing related knowledge, which provides a new avenue for constructing an accurate and efficient digital twin of wind turbine gearboxes.</p>

A CapsNet-Based Fault Diagnosis Method for a Digital Twin of a Wind Turbine Gearbox

Paper Proceedings: https://asmedigitalcollection.asme.org/POWER/POWER2021/volume/85109

ASME 2021 Power Conference (POWER2021)

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