Patents by Inventor Shuangjie LIU

Shuangjie LIU has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions
    Patent number: 12044595
    Abstract: The present invention provides a dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions, including acquiring bearing vibration data under different working conditions to obtain a source domain sample and a target domain sample; establishing a deep convolutional neural network model with dynamic joint distribution alignment; feeding both the source domain sample and the target domain sample into the deep convolutional neural network model with initialized parameters, and extracting, by a feature extractor, high-level features of the source domain sample and the target domain sample; calculating a marginal distribution distance and a conditional distribution distance; obtaining a joint distribution distance according to the marginal distribution distance and the conditional distribution distance, and combining the joint distribution distance and a label loss to obtain a target function; and optimizing the target function by using SGD, and training the
    Type: Grant
    Filed: January 13, 2021
    Date of Patent: July 23, 2024
    Assignee: SOOCHOW UNIVERSITY
    Inventors: Changqing Shen, Shuangjie Liu, Xu Wang, Dong Wang, Yongjun Shen, Zaigang Chen, Aiwen Zhang, Xingxing Jiang, Juanjuan Shi, Weiguo Huang, Jun Wang, Guifu Du, Zhongkui Zhu
  • DYNAMIC JOINT DISTRIBUTION ALIGNMENT NETWORK-BASED BEARING FAULT DIAGNOSIS METHOD UNDER VARIABLE WORKING CONDITIONS
    Publication number: 20230168150
    Abstract: The present invention provides a dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions, including acquiring bearing vibration data under different working conditions to obtain a source domain sample and a target domain sample; establishing a deep convolutional neural network model with dynamic joint distribution alignment; feeding both the source domain sample and the target domain sample into the deep convolutional neural network model with initialized parameters, and extracting, by a feature extractor, high-level features of the source domain sample and the target domain sample; calculating a marginal distribution distance and a conditional distribution distance; obtaining a joint distribution distance according to the marginal distribution distance and the conditional distribution distance, and combining the joint distribution distance and a label loss to obtain a target function; and optimizing the target function by using SGD, and training the
    Type: Application
    Filed: January 13, 2021
    Publication date: June 1, 2023
    Inventors: Changqing SHEN, Shuangjie LIU, Xu WANG, Dong WANG, Yongjun SHEN, Zaigang CHEN, Aiwen ZHANG, Xingxing JIANG, Juanjuan SHI, Weiguo HUANG, Jun WANG, Guifu DU, Zhongkui ZHU