Patents by Inventor Piao LEI

Piao LEI 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).

  • Adaptive manifold probability distribution-based bearing fault diagnosis method
    Patent number: 11644383
    Abstract: The invention provides an adaptive manifold probability distribution-based bearing fault diagnosis method, including constructing transferable domains and transfer tasks; converting a data sample in each transfer task into frequency domain data via Fourier transform, inputting the frequency domain data into a GFK algorithm model, and calculating a manifold feature representation matrix related to a bearing fault in each transfer task by using the GFK algorithm model; calculating a cosine distance between centers of a target domain and a source domain in each transfer task according to a manifold feature representation, and defining a target function of in-domain classifier learning; then solving the target function, to obtain a probability distribution matrix of the target domain; and selecting a label corresponding to the largest probability value corresponding to each data sample in the target domain from the probability distribution matrix as a predicted label of the data sample in the target domain.
    Type: Grant
    Filed: November 26, 2020
    Date of Patent: May 9, 2023
    Assignee: SOOCHOW UNIVERSITY
    Inventors: Changqing Shen, Yu Xia, Lin Kong, Liang Chen, Piao Lei, Yongjun Shen, Dong Wang, Hongbo Que, Aiwen Zhang, Minjie Chen, Chuancang Ding, Xingxing Jiang, Jun Wang, Juanjuan Shi, Weiguo Huang, Zhongkui Zhu
  • ADAPTIVE MANIFOLD PROBABILITY DISTRIBUTION-BASED BEARING FAULT DIAGNOSIS METHOD
    Publication number: 20220373430
    Abstract: The invention provides an adaptive manifold probability distribution-based bearing fault diagnosis method, including constructing transferable domains and transfer tasks; converting a data sample in each transfer task into frequency domain data via Fourier transform, inputting the frequency domain data into a GFK algorithm model, and calculating a manifold feature representation matrix related to a bearing fault in each transfer task by using the GFK algorithm model; calculating a cosine distance between centers of a target domain and a source domain in each transfer task according to a manifold feature representation, and defining a target function of in-domain classifier learning; then solving the target function, to obtain a probability distribution matrix of the target domain; and selecting a label corresponding to the largest probability value corresponding to each data sample in the target domain from the probability distribution matrix as a predicted label of the data sample in the target domain.
    Type: Application
    Filed: November 26, 2020
    Publication date: November 24, 2022
    Inventors: Changqing SHEN, Yu XIA, Lin KONG, Liang CHEN, Piao LEI, Yongjun SHEN, Dong WANG, Hongbo QUE, Aiwen ZHANG, Minjie CHEN, Chuancang DING, Xingxing JIANG, Jun WANG, Juanjuan SHI, Weiguo HUANG, Zhongkui ZHU