The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jan. 30, 2024
Filed:
Aug. 04, 2020
Soochow University, Suzhou, CN;
Changqing Shen, Suzhou, CN;
Xu Wang, Suzhou, CN;
Jing Xie, Suzhou, CN;
Aiwen Zhang, Suzhou, CN;
Dong Wang, Suzhou, CN;
Xiaofeng Shang, Suzhou, CN;
Dongmiao Song, Suzhou, CN;
Xingxing Jiang, Suzhou, CN;
Jun Wang, Suzhou, CN;
Juanjuan Shi, Suzhou, CN;
Weiguo Huang, Suzhou, CN;
Zhongkui Zhu, Suzhou, CN;
SOOCHOW UNIVERSITY, Suzhou, CN;
Abstract
The invention relates to a fault diagnosis method for a rolling bearing under variable working conditions. Based on a convolutional neural network, a transfer learning algorithm is combined to handle the problem of the reduced universality of deep learning models. Data acquired under different working conditions is segmented to obtain samples. The samples are preprocessed by using FFT. Low-level features of the samples are extracted by using improved ResNet-50, and a multi-scale feature extractor analyzes the low-level features to obtain high-level features as inputs of a classifier. In a training process, high-level features of training samples and test samples are extracted, and a conditional distribution distance between them is calculated as a part of a target function for backpropagation to implement intra-class adaptation, thereby reducing the impact of domain shift, to enable a deep learning model to better carry out fault diagnosis tasks.