Adaptive manifold probability distribution-based bearing fault diagnosis method

• Main Authors: Huang, Weiguo, Chen, Minjie, Zhang, Aiwen, Wang, Dong, Wang, Jun, Shen, Changqing, Kong, Lin, Chen, Liang, Shen, Yongjun, Ding, Chuancang, Xia, Yu, Que, Hongbo, Jiang, Xingxing, Lei, Piao, Zhu, Zhongkui, Shi, Juanjuan
• Format: Patent
• Language: English
• Published: 09.05.2023
• Subjects: MEASURING; PHYSICS; TESTING; TESTING STATIC OR DYNAMIC BALANCE OF MACHINES ORSTRUCTURES; TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR

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.