Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2025 , Vol. 59 >Issue 5: 561 - 568

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.375

New Type Power System and the Integrated Energy

A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning

  • PAN Meiqi ,
  • HE Xing
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  • a. College of Smart Energy, Shanghai Jiao Tong University, Shanghai 200240, China
    b. Key Laboratory of Control of Power Transmission and Conversion of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 2023-08-07

  Revised date: 2023-11-29

  Accepted date: 2023-12-04

  Online published: 2023-12-19

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Abstract

In engineering practice, wind turbine fault diagnosis encounters situations where the fault category in the training data is different from the actual one. To diagnose unknown wind turbine faults, it is necessary to transfer the fault feature information learned during training to the unknown fault category. Unlike traditional methods that directly establish mapping between fault samples and fault categories, a zero-shot learning (ZSL) method for wind turbine fault diagnosis based on fault attributes is proposed to enable fault feature migration. A fault attribute matrix is established by describing the attributes of each fault, which is embedded into the fault sample space and fault category space. Then, a fault attribute learner is developed based on convolutional neural network (CNN), and a fault classifier is established based on Euclidean distance, forming the diagnosis process where fault attributes are predicted from fault samples and then classified. Finally, the effectiveness and superiority of the proposed fault diagnosis method are validated by comparing it with other zero-shot learning methods.

Key words： wind turbine fault diagnosis; zero-shot learning (ZSL); convolutional neural network (CNN); knowledge-data hybrid driven

Cite this article

PAN Meiqi , HE Xing . A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning[J]. Journal of Shanghai Jiaotong University, 2025 , 59(5) : 561 -568 . DOI: 10.16183/j.cnki.jsjtu.2023.375

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