Complementary-Label Adversarial Domain Adaptation Fault Diagnosis Network under Time-Varying Rotational Speed and Weakly-Supervised Conditions

作     者：Siyuan Liu Jinying Huang Jiancheng Ma Licheng Jing Yuxuan Wang 

作者机构：School of Data Science and TechnologyNorth University of ChinaTaiyuan030051China School of Mechanical EngineeringNorth University of ChinaTaiyuan030051China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第79卷第4期

页      面：761-777页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Shanxi Scholarship Council of China(2022-141) Fundamental Research Program of Shanxi Province(202203021211096) 

主　　题：Time-varying rotational speed weakly-supervised fault diagnosis domain adaptation 

摘      要：Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample *** engineering practice,the rotational speed of the machine is often transient and time-varying,which makes the sample annotation increasingly ***,the number of samples collected from different health states is often *** deal with the above challenges,a complementary-label(CL)adversarial domain adaptation fault diagnosis network(CLADAN)is proposed under time-varying rotational speed and weakly-supervised *** the weakly supervised learning condition,machine prior information is used for sample annotation via cost-friendly complementary label learning.A diagnosticmodel learning strategywith discretized category probabilities is designed to avoidmulti-peak distribution of prediction *** adversarial training process,we developed virtual adversarial regularization(VAR)strategy,which further enhances the robustness of the model by adding adversarial perturbations in the target *** experiments on two case studies validated the superior performance of the proposed method.