摘要
基于深度学习的故障诊断方法被广泛应用于以轴承为代表的机械关键部件故障诊断,其取得理想效果的前提是有足量故障样本且训练集、测试集满足同分布要求。然而,在实际工况下数据分布会发生变化,进而使原有工况下的诊断模型很难适用于新工况。为此,域适应类迁移学习方法被用于解决训练集、测试集分布不同的问题,其重点在于实现数据分布适应,即度量数据分布差异,并利用度量结果对模型训练进行指导,从而提升学习效率和诊断准确率。在此基础上,提出了一种基于对抗学习的域适应方法,该方法的核心是将提出的指数调节策略与对抗网络相结合,使得网络在故障诊断过程中更有针对性地适应目标域的数据分布。该网络由特征提取器、分类器、一个全局域鉴别器和多个局部域鉴别器组成,利用对抗策略和适应性矩估计算法对模型进行优化,并通过基于指数调节策略设定的指数自适应因子对模型中的边缘分布和条件分布重要性进行调节,使得模型可以稳定、高效地进行故障诊断。在跨转速、跨负载和同时跨转速和负载的轴承诊断案例中对提出的方法进行验证,结果表明本文方法的诊断效果优于其他域适应方法,并具有较好的稳定性。
The fault diagnosis method based on deep learning is widely used in the fault diagnosis of key mechanical components represented by bearings.The premise of achieving ideal results is that there are enough fault samples and the training set and test set meet the same distribution requirements.However,the data distribution will change under the actual working conditions,which makes it difficult to apply the diagnostic model under the original working conditions to the new working conditions.For this reason,the domain adaptation transfer learning method is used to solve the problem of different distribution of training sets and test sets,and its key point is to achieve data distribution adaptation,that is,to measure data distribution differences and use the mea-surement results to guide model training,which can effectively improve learning efficiency and diagnostic accuracy.On this basis,this paper proposes a new domain adaptation method based on adversarial learning.The core of this method is to combine the pro-posed exponential adjustment strategy with adversarial network to make the network adapt to different data distribution in source domain and target domain more specifically in the process of fault diagnosis.The network consists of a feature extractor,a classifi-er,a global domain discriminator,and multiple local domain discriminators,and the model is optimized by using the adversarial strategy and adaptive moment estimation algorithm,and adjusted the importance of marginal distribution and conditional distribu-tion by using the exponential adaptive factor set based on the exponential adjustment strategy,so that the model could diagnose faults stably and efficiently.The proposed method is verified in bearing diagnosis cases of cross-speed,cross-load and simultaneous cross-speed load.The results show that the method in this paper is better than other domain adaptation methods in diagnosis effect and has better stability.
作者
田静
沈长青
陈再刚
石娟娟
江星星
朱忠奎
TIAN Jing;SHEN Chang-qing;CHEN Zai-gang;SHI Juan-juan;JIANG Xing-xing;ZHU Zhong-kui(State Key Laboratory of Rail Transit Vehicle System,Southwest Jiaotong University,Chengdu 610031,China;Department of Vehicle Engineering,School of Rail Transportation,Soochow University,Suzhou 215131,China)
出处
《振动工程学报》
EI
CSCD
北大核心
2024年第3期476-484,共9页
Journal of Vibration Engineering
基金
国家自然科学基金资助项目(52272440)
苏州市前沿技术研究项目(SYG202323)
轨道交通运载系统全国重点实验室开放课题(TPL2105)。
关键词
故障诊断
轴承
域适应
对抗学习
fault diagnosis
bearing
domain adaptation
adversarial learning
作者简介
田静(1998-),女,硕士研究生。E-mail:jtian2020@stu.suda.edu.cn;通讯作者:沈长青(1987-),男,博士,教授。E-mail:cqshen@suda.edu.cn。
