液压与气动2026，Vol.50Issue(8)：75-89,15.DOI:10.11832/j.issn.1000-4858.2026.08.008
基于元学习的小样本跨域自适应方法在液压泵故障诊断中的应用
Application of Meta-learning-based Few-shot Cross-domain Adaptation Method to Hydraulic Pump Fault Diagnosis
摘要
Abstract
To address the problems of scarce fault samples and insufficient cross-condition generalization in hydraulic pump fault diagnosis,we propose a meta-learning-based few-shot cross-domain adaptation method.First,different pressure-flow combinations are defined as operating-condition domains.Then,a multi-scale one-dimensional convolutional neural network is employed as the feature extractor to learn highly discriminative multi-scale features directly from raw vibration signals.During meta-training,the bi-level optimization mechanism of model-agnostic meta-learning enables the model to rapidly adapt to unseen operating conditions using only a few support samples.To further improve robustness,a dual-objective loss function is constructed by combining a label-smoothed classification loss with an L2 regularization term,thereby alleviating overfitting and enhancing cross-domain stability.Experiments are conducted on two unseen target operating-condition domains with multi-episodic evaluation.After five adaptation steps,the proposed method achieves average accuracies of 98.90%and 96.80%,respectively.Compared with existing methods,the proposed method reduces dependence on training samples while maintaining high diagnostic accuracy,demonstrating its effectiveness and practical potential in few-shot cross-condition fault classification.关键词
模型无关元学习/跨域适应/小样本/多尺度卷积神经网络/故障诊断/液压泵Key words
model-agnostic meta-learning/cross-domain adaptation/few-shot/multi-scale convolutional neural network/fault diagnosis/hydraulic pump分类
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姜万录,唐恩宇,赵阳,马帅阳,曾令辉..基于元学习的小样本跨域自适应方法在液压泵故障诊断中的应用[J].液压与气动,2026,50(8):75-89,15.基金项目
国家自然科学基金(52275067) （52275067）
河北省自然科学基金(E202320303) （E202320303）
