基于深度学习的旋转机械小样本故障诊断方法研究综述  

A review of deep learning-based few sample fault diagnosis method for rotating machinery

作　　者：吴轲 吴军[1] 舒启明 沈卫明 宋文斌 WU Ke;WU Jun;SHU Qiming;SHEN Weiming;SONG Wenbin(School of Naval Architecture and Ocean Engineering,Huazhong University of Science and Technology,Wuhan 430074,China;School of Mechanical Science&Engineering,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]华中科技大学船舶与海洋工程学院,湖北武汉430074 [2]华中科技大学机械科学与工程学院,湖北武汉430074

出　　处：《中国舰船研究》2025年第2期3-19,共17页Chinese Journal of Ship Research

基　　金：国家自然科学基金项目(523B2100);华中科技大学交叉研究支持计划项目(2024JCYJ028)。

摘　　要：[目的]深度学习在旋转机械故障诊断领域展示出显著潜力,但因工程实践中训练样本难以获取,导致基于深度学习的故障诊断方法存在泛化性弱、诊断精度低等问题。小样本故障诊断方法,凭借在有限数据条件下故障信息有效挖掘的能力,逐渐成为学术界和工程界研究的热点。[方法]通过回顾并总结小样本学习方法在旋转机械故障诊断中的最新研究成果,阐述小样本故障诊断的任务定义和主要学习方法。在此基础上,根据不同的技术原理,将现有小样本故障诊断方法归纳为元学习、迁移学习、领域泛化、数据增强和自监督学习5类,并分析各类方法原理、应用及优缺点。[结果]各类方法在小样本故障诊断领域已取得一定成效,但在实际应用中仍存在诸多局限性,如元学习计算资源需求大、迁移学习受域间相似性限制等。[结论]未来在小样本故障诊断领域应探索数据治理、多模态学习、联邦学习以及机理-数据混合驱动等方法,克服现有方法的局限性,进一步提升小样本故障诊断的可靠性。[Objectives]Deep learning has shown great potential in the field of rotating machinery fault diagnosis.Its excellent performance heavily relies on sufficient training samples.However,in practical engineering applications,acquiring sufficient training data is particularly difficult,resulting in poor generalization capability and low diagnostic accuracy.Therefore,few-sample fault diagnosis methods,which can effectively extract fault-related information from limited data,have gradually become a research focus in both academic and engineering circles.[Method]In this paper,the latest achievements in few-sample fault diagnosis of rotating machinery are reviewed and summarized.This paper describes the definition and learning methods for few-sample fault diagnosis.Few-sample fault diagnosis methods aim to effectively develop fault diagnosis models with strong generalization capability under limited training data conditions.Currently,according to different technical principles,existing few-sample fault diagnosis methods can be classified into five categories:meta-learning,transfer learning,domain generalization,data augmentation,and self-supervised learning.Subsequently,this paper elaborates on the applications of these five methods in rotating machinery fault diagnosis.Meta-learning-based fault diagnosis methods improve the ability of models to rapidly learn and adapt to new tasks by acquiring common knowledge from multiple related tasks.The transfer learning-based fault diagnosis methods achieve knowledge migration from the source domain to the target domain using unsupervised domain adaptation techniques.The domain generalization-based fault diagnosis methods train models using single or multiple source domains and enable the model to learn features that are common across those domains.The data augmentation-based fault diagnosis methods expand the original dataset by generating models.The self-supervised learning-based fault diagnosis methods exploit the structural information of data to construct pseudo-labels.[Result

关 键 词：旋转机械 故障分析 故障诊断 小样本 元学习 迁移学习 领域泛化 数据增强 自监督学习 

分 类 号：U664.21[交通运输工程—船舶及航道工程] U664.22[交通运输工程—船舶与海洋工程]