A Gaussian-guided adversarial adaptation transfer network for rolling bearing fault diagnosis

被引:30
|
作者
Wu, Zhenghong [1 ]
Jiang, Hongkai [1 ]
Liu, Shaowei [1 ]
Yang, Chunxia [2 ]
机构
[1] Northwestern Polytech Univ, Sch Civil Aviat, Xian 710072, Peoples R China
[2] COMAC Flight Test Ctr, Shanghai 201207, Peoples R China
基金
中国国家自然科学基金;
关键词
Bearing fault diagnosis; Task-specific decision boundary; Gaussian-guided distribution alignment; Novel adversarial training mechanism; TRANSFER LEARNING-METHOD;
D O I
10.1016/j.aei.2022.101651
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Most current unsupervised domain networks try to alleviate domain shifts by only considering the difference between source domain and target domain caused by the classifier, without considering task-specific decision boundaries between categories. In addition, these networks aim to completely align data distributions, which is difficult because each domain has its characteristics. In light of these issues, we develop a Gaussian-guided adversarial adaptation transfer network (GAATN) for bearing fault diagnosis. Specifically, GAATN introduces a Gaussian-guided distribution alignment strategy to make the data distribution of two domains close to the Gaussian distribution to reduce data distribution discrepancies. Furthermore, GAATN adopts a novel adversarial training mechanism for domain adaptation, which designs two task-specific classifiers to identify target data to consider the relationship between target data and category boundaries. Massive experimental results prove that the superiority and robustness of the proposed method outperform existing popular methods.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Imbalance domain adaptation network with adversarial learning for fault diagnosis of rolling bearing
    Zhu, Hongqiu
    Huang, Ziyi
    Lu, Biliang
    Cheng, Fei
    Zhou, Can
    [J]. SIGNAL IMAGE AND VIDEO PROCESSING, 2022, 16 (08) : 2249 - 2257
  • [2] Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis
    Zhao, Ke
    Jiang, Hongkai
    Wang, Kaibo
    Pei, Zeyu
    [J]. KNOWLEDGE-BASED SYSTEMS, 2021, 222
  • [3] Imbalance domain adaptation network with adversarial learning for fault diagnosis of rolling bearing
    Hongqiu Zhu
    Ziyi Huang
    Biliang Lu
    Fei Cheng
    Can Zhou
    [J]. Signal, Image and Video Processing, 2022, 16 : 2249 - 2257
  • [4] Rolling bearing transfer fault diagnosis method based on adversarial variational autoencoder network
    Zou, Yisheng
    Shi, Keming
    Liu, Yongzhi
    Ding, Guofu
    Ding, Kun
    [J]. MEASUREMENT SCIENCE AND TECHNOLOGY, 2021, 32 (11)
  • [5] Deep domain adaptation with adversarial idea and coral alignment for transfer fault diagnosis of rolling bearing
    Li, Ranran
    Li, Shunming
    Xu, Kun
    Lu, Jiantao
    Teng, Guangrong
    Du, Jun
    [J]. MEASUREMENT SCIENCE AND TECHNOLOGY, 2021, 32 (09)
  • [6] A simulation-data-driven subdomain adaptation adversarial transfer learning network for rolling element bearing fault diagnosis
    Zhu, Peng
    Dong, Shaojiang
    Pan, Xuejiao
    Hu, Xiaolin
    Zhu, Sunke
    [J]. MEASUREMENT SCIENCE AND TECHNOLOGY, 2022, 33 (07)
  • [7] Conditional distribution-guided adversarial transfer learning network with multi-source domains for rolling bearing fault diagnosis
    Wu, Zhenghong
    Jiang, Hongkai
    Liu, Shaowei
    Liu, Yunpeng
    Yang, Wangfeng
    [J]. ADVANCED ENGINEERING INFORMATICS, 2023, 56
  • [8] Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis
    Wang, Xiaodong
    Liu, Feng
    [J]. SENSORS, 2020, 20 (01)
  • [9] Internal adversarial guided unsupervised multi-domain adaptation network for collaborative fault diagnosis of bearing
    Shao, HaiDong
    Chen, XingKai
    Cao, HongRu
    Jiang, HongKai
    [J]. Zhongguo Kexue Jishu Kexue/Scientia Sinica Technologica, 2023, 53 (07): : 1229 - 1240
  • [10] Bearing fault diagnosis based on partial domain adaptation adversarial network
    Zhou, Huafeng
    Cheng, Peiyuan
    Shao, Siyu
    Zhao, Yuwei
    Yang, Xinyu
    [J]. MEASUREMENT SCIENCE AND TECHNOLOGY, 2022, 33 (12)