首页|基于元迁移学习的智能故障诊断方法

基于元迁移学习的智能故障诊断方法

Intelligent Fault Diagnosis Method Based on Meta-Transfer Learning

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工程实践过程中,获取大量的故障样本可能是困难和昂贵的,即可用于训练的故障样本相对较少,导致采用传统深度学习方法训练的故障诊断模型难以快速适应新的工况.为此,提出了一种基于元迁移学习的智能故障诊断方法.首先,对精心设计的能有效提取故障特征的自注意力网络(self-at-tention network,SAN)进行预训练;然后,在每个"N-way M-shot"元任务上训练一个基学习器和元学习器,以自适应调整预先训练好的SAN模型的参数;最后,通过不同的小样本故障诊断(few-shot fault diagnosis,FSFD)任务验证所提方法的有效性.结果表明,该方法在跨工况FSFD任务下取得了较高的诊断精度.
In practical engineering,it may be difficult and expensive to acquire numerous fault samples,that is,the fault samples used for training are relatively few,which makes it difficult for the fault diagnosis mod-els trained by the traditional deep learning methods to quickly adapt to the new working conditions.There-fore,an intelligent fault diagnosis method based on meta-transfer learning is proposed.Firstly,a well-de-signed self-attention network(SAN)that effectively extracts fault features is pre-trained.Secondly,a base-leaner and a meta-learner are trained on each"N-way M-shot"meta-task to adaptively adjust the parame-ters of the pre-trained SAN model.Finally,the effectiveness of the proposed method is verified through dif-ferent few-shot fault diagnosis(FSFD)tasks.The results prove that the method achieves high diagnosis ac-curacies under cross-working condition FSFD tasks.

meta-transfer learningself-attention networkrolling bearingfault diagnosisfew-shot learning

黄乐、万烂军、倪炜

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湖南工业大学计算机学院,株洲 412007

元迁移学习 自注意力网络 滚动轴承 故障诊断 小样本学习

湖南省自然科学基金面上项目湖南省教育厅优秀青年项目国家自然科学基金青年科学基金项目湖南省教育厅重点项目

2023JJ3021721B05476170217722A0408

2024

组合机床与自动化加工技术
大连组合机床研究所 中国机械工程学会生产工程分会

组合机床与自动化加工技术

CSTPCD北大核心
影响因子:0.671
ISSN:1001-2265
年,卷(期):2024.(9)
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