Two-head classifier guided domain adversarial learning for universal domain adaptation in intelligent fault diagnosis

被引:1
|
作者
Zhang, Jiyang [1 ]
Wang, Xiangxiang [1 ]
Su, Zhiheng [1 ]
Lian, Penglong [1 ]
Xu, Hongbing [1 ]
Zou, Jianxiao [1 ,2 ]
Fan, Shicai [1 ,2 ]
机构
[1] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Sichuan, Peoples R China
[2] Univ Elect Sci & Technol China, Shenzhen Inst Adv Study, Shenzhen 518110, Guangdong, Peoples R China
关键词
Intelligent fault diagnosis; Cross-domain fault diagnosis; Transfer learning; Universal domain adaptation; Two-head classifier; Domain adversarial learning; NETWORK;
D O I
10.1016/j.ress.2024.110708
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Domain adaptation (DA) methods have been widely used in cross-domain fault diagnosis to mitigate the distribution discrepancy between data from different working conditions. However, traditional DA methods are designed for specific one known category shift between domains. When prior knowledge about relationships between source and target label sets is unknown, the applicability of these methods is limited. To address this issue, a universal domain adaptation method named two-head classifier guided domain adversarial learning (THC-DAN) is proposed, which can handle all category shift scenarios in DA, including closed-set, partial-set, open-set, and open-partial-set. Specifically, we develop a domain adversarial network with an elegantly designed two-head classifier and adapt it to target domain. During adaptation, we first introduce an informative consistency score based on the two-head classifier to distinguish target private samples. Then, the consistency separation loss is proposed to push these samples away from classification boundaries. Finally, to realize the safe alignment on common classes between domains, the weighted adversarial learning based on the two-head classifier's prediction probability is presented to weaken effects of source private samples. Experiments under all DA scenarios on datasets from Case Western Reserve University, Paderborn University, and our own Drivetrain Prognostics Simulator demonstrate the effectiveness of THC-DAN.
引用
收藏
页数:18
相关论文
共 50 条
  • [41] Unsupervised Method Based on Adversarial Domain Adaptation for Bearing Fault Diagnosis
    Li, Yao
    Yang, Rui
    Wang, Hongshu
    APPLIED SCIENCES-BASEL, 2023, 13 (12):
  • [42] Uncertainty-guided adversarial augmented domain networks for single domain generalization fault diagnosis
    Jiang, Dongnian
    He, Chenxian
    Li, Wei
    Xu, Dezhi
    MEASUREMENT, 2025, 241
  • [43] A novel hybrid distance guided domain adversarial method for cross domain fault diagnosis of gearbox
    Jiang, Xingwang
    Wang, Xiaojing
    Han, Baokun
    Wang, Jinrui
    Zhang, Zongzhen
    Ma, Hao
    Xing, Shuo
    Man, Kai
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2023, 34 (06)
  • [44] A New Probability Guided Domain Adversarial Network for Bearing Fault Diagnosis
    Li, Jingde
    Chen, Bojian
    Shen, Changqing
    Wang, Dong
    Shi, Juanjuan
    Jiang, Xingxing
    IEEE SENSORS JOURNAL, 2023, 23 (02) : 1462 - 1470
  • [45] Crucial Semantic Classifier-based Adversarial Learning for Unsupervised Domain Adaptation
    Zhang, Yumin
    Gao, Yajun
    Li, Hongliu
    Yin, Ating
    Zhang, Duzhen
    Chen, Xiuyi
    2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, 2023,
  • [46] Domain Adversarial Reinforcement Learning for Partial Domain Adaptation
    Chen, Jin
    Wu, Xinxiao
    Duan, Lixin
    Gao, Shenghua
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (02) : 539 - 553
  • [47] Deep multi-scale adversarial network with attention: A novel domain adaptation method for intelligent fault diagnosis*
    Zhao, Bo
    Zhang, Xianmin
    Zhan, Zhenhui
    Wu, Qiqiang
    JOURNAL OF MANUFACTURING SYSTEMS, 2021, 59 : 565 - 576
  • [48] Discrepant Adversarial Domain Adaptation Network for Rolling Bearing Intelligent Fault Diagnosis under Varying Working Condition
    Zheng, Kai
    Zhao, Pengyuan
    Xiong, Jinfeng
    Bai, Yin
    Li, Yongying
    Long, Zihao
    Zhang, Zheng
    ENGINEERING LETTERS, 2025, 33 (04) : 860 - 875
  • [49] A Universal Domain Adaptation Method With Cluster Matching for Machinery Fault Diagnosis
    Lin, Fanwei
    Guo, Chang
    Zhao, Zhibin
    Zhang, Xingwu
    Chen, Xuefeng
    Tao, Zhiyu
    IEEE INTERNET OF THINGS JOURNAL, 2025, 12 (06): : 7487 - 7502
  • [50] A universal fault diagnosis framework for marine machinery based on domain adaptation
    Guo, Yu
    Zhang, Jundong
    Sun, Bin
    Wang, Yongkang
    OCEAN ENGINEERING, 2024, 302