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A Generalize Mechanical Fault Diagnosis Method Based on Enhanced Meta Learning
编号：131 访问权限：仅限参会人 更新：2025-11-10 15:52:08 浏览：22次 张贴报告

报告开始：暂无开始时间（Asia/Shanghai）

报告时间：暂无持续时间

所在会场：[暂无会议] [暂无会议段]

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摘要
Mechanical fault diagnosis faces dynamic and complex fault patterns and environmental variations, requiring diagnostic models with high generalization capability. To mitigate the data distribution shift under different operating conditions and address the class imbalance caused by the high cost of data collection, this paper proposes an enhanced meta learning framework. From a gradient representation perspective, this framework learns generalized boundaries across all diagnostic tasks by coordinating inter-domain and inter-class gradients, thereby improving the recognition of unknown classes under imbalanced conditions. Furthermore, based on this enhanced meta-learning framework, a joint learning paradigm involving open- and closed-set classifiers is developed to balance the decision boundaries between known and unknown classes, enabling the model to quickly adapt to unfamiliar domains. The superior performance of the proposed framework is validated on the publicly recognized HIT bearing dataset.
关键词
class imbalance, fault diagnosis, meta learning, rotating machinery
报告人
Yan Ge
senior engineer China Institute of Marine Technology & Economy

稿件作者
Yang Song China State Shipbuilding Corporation Limited
Qingyuan Cao Beijing Institute of Structure and Environment Engineering
Yan Ge China Institute of Marine Technology & Economy
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重要日期

  • 会议日期

    11月21日

    2025

    至

    11月23日

    2025

  • 10月20日 2025

    初稿截稿日期

  • 11月23日 2025

    注册截止日期

主办单位
IEEE Instrumentation and Measurement Society
South China University of Technology
承办单位
South China University of Technology
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