机械科学与技术2026，Vol.45Issue(2)：207-215,9.DOI:10.13433/j.cnki.1003-8728.20240025
小样本下一阶元学习在滚动轴承故障诊断中的应用
Application of First-order Meta-learning in Rolling Bearing Fault Diagnosis Under Small-sample Conditions
摘要
Abstract
To address the issue of model-agnostic meta-learning networks incurring overfitting due to high model complexity during training in small-sample conditions,we propose a first-order meta-learning-based rolling bearing fault diagnosis model.Initially,we form meta-tasks by randomly sampling original signals following a meta-learning strategy.Subsequently,within meta-tasks associated with known working conditions,we employ a wide-kernel convolutional network to enhance the model's capability to extract fault information from one-dimensional vibration signals,acquiring meta-knowledge.Additionally,to reduce model training complexity and mitigate overfitting,we employ a gradient-based model optimization,progressively adjusting from initial parameters to optimal training weights.Finally,leveraging the acquired meta-knowledge,our approach can realize rapid and accurate fault classification under unknown working conditions.The experimental results show that the proposed method achieves the highest diagnostic accuracy on both validations bearing datasets,proving the effectiveness and superiority of the proposed method.关键词
滚动轴承/故障诊断/宽卷积核/小样本/元学习Key words
rolling bearing/fault diagnosis/wide convolution kernels/small samples/meta learning分类
机械制造引用本文复制引用
杨文龙,王波,张猛,徐浩,汪超..小样本下一阶元学习在滚动轴承故障诊断中的应用[J].机械科学与技术,2026,45(2):207-215,9.基金项目
安徽省高校科研重点项目(2025AHGXZK30014)与滁州学院科研启动基金项目(2024qd22) （2025AHGXZK30014）
