上海交通大学学报

上海交通大学学报

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基于零样本相关判别空间适应的船舶轴系故障诊断

  

  1. 1.上海海事大学 物流工程学院，上海  201306；

    2.上海海事大学 物流科学与工程研究院，上海  201306；

    3.上海江南长兴造船有限责任公司，上海  201913

  • 作者简介:周煊淇（2000－），硕士生，从事船舶推进轴系故障诊断研究
  • 基金资助:
    船舶工业技术能力提升专项(QT1451-0415043)资助项目

Ship Shafting Fault Diagnosis Based on Zero-Shot Guided Discriminative Adaptation

  1. 1.    Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China; 

    2. Institution of Logistics Science & Engineering, Shanghai Maritime University, Shanghai 201306, China; 

    3. Shanghai Jiangnan-Changxing Shipbuilding Co., Ltd., Shanghai 201913, China

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摘要： 针对船舶推力轴承在跨设备、跨工况场景下目标域标签样本稀缺导致难以跨域诊断的问题，提出一种基于零样本相关判别空间适应的船舶推力轴承故障诊断方法（Zero-Shot Guided Discriminative Adaptation，ZSGDA）。首先提取源域与目标域的任务无关样本对特征作为先验知识，结合源域有标签故障样本构建拟合目标域分布的特征子空间；其次，引入子空间引导判别优化框架（GDCSL）规划特征映射路径，优化跨域数据的共享特征空间分布；最后，在目标域零标签条件下实现故障特征到语义空间的鲁棒映射。利用轴承数据集设计实验验证所提方法在零样本场景下平均诊断精度达99.3%，且能大幅度缩短收敛周期。该方法实现船舶推力轴承的零样本迁移，为零样本场景下船舶推力轴承故障诊断提供了高精度、高鲁棒性的解决方案，具有显著工程应用价值。

关键词: 故障诊断, 子空间学习, 零样本, 船舶轴系, 领域适应

Abstract: To address the challenge of cross-domain fault diagnosis for ship thrust bearings caused by scarce labeled samples in the target domain under cross-equipment and cross-operational conditions, a fault diagnosis method based on Zero-Shot Guided Discriminative Adaptation (ZSGDA) is proposed. The framework initially extracts cross-domain task-irrelevant feature pairs as prior knowledge, which are jointly optimized with labeled source fault samples to build distribution-consistent feature subspaces for the target domain. Additionally, a Guided Discriminative and Correlation Subspace Learning (GDCSL) framework is introduced to plan the feature mapping path and optimize the distribution of the shared feature space for cross-domain data. Finally, a robust mapping from fault features to the semantic space is achieved under the condition of zero labels in the target domain. Experiments designed using bearing datasets verify that the proposed method achieves an average diagnostic accuracy of 99.3% in zero-shot scenarios and can significantly shorten the convergence cycle. This method realizes zero-shot transfer for ship thrust bearings, providing a high-precision and high-robustness solution for fault diagnosis of ship thrust bearings in zero-shot scenarios, with significant engineering application value.

Key words: fault diagnosis, subspace learning, zero-shot, ship shafting, domain adaptation

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