Soochow University

Lin Kong

Jun Wang

Yu Xia

Liang Chen

Weiguo Huang

Zhongkui Zhu

Xingxing Jiang

Dong Wang

Changqing Shen

Juanjuan Shi

Aiwen Zhang

Yongjun Shen

Piao Lei

Minjie Chen

Hongbo Que

Chuancang Ding

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.

Adaptive manifold probability distribution-based bearing fault diagnosis method

Growing community of inventors

Suzhou, China

Lin Kong