Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Chuancang Ding"'
Publikováno v:
Sensors, Vol 24, Iss 16, p 5165 (2024)
Rolling bearing fault diagnosis methods based on transfer learning always assume that the sample classes in the target domain are consistent with those in the source domain during the training phase. However, it is difficult to collect all fault clas
Externí odkaz:
https://doaj.org/article/55a387ebc0b5471daed8b5bd4a3caee7
Publikováno v:
Machines, Vol 11, Iss 6, p 626 (2023)
Due to their advantages of compact size, high reduction ratio, large stiffness and high load capacity, RV reducers have been widely used in industrial robots. The dynamic characteristics of RV reducers in terms of vibratory response and dynamic trans
Externí odkaz:
https://doaj.org/article/8e46fe5e00ec48afa9e3f2b3f41d38b9
Publikováno v:
IEEE Transactions on Industrial Informatics. 18:7198-7207
Publikováno v:
Machines; Volume 11; Issue 6; Pages: 626
Due to their advantages of compact size, high reduction ratio, large stiffness and high load capacity, RV reducers have been widely used in industrial robots. The dynamic characteristics of RV reducers in terms of vibratory response and dynamic trans
Publikováno v:
IEEE Transactions on Industrial Electronics. 69:6267-6277
Publikováno v:
Engineering Applications of Artificial Intelligence. 123:106477
Publikováno v:
Neural Networks. 145:331-341
Driven by industrial big data and intelligent manufacturing, deep learning approaches have flourished and yielded impressive achievements in the community of machine fault diagnosis. Nevertheless, current diagnosis models trained on a specific datase
Publikováno v:
2022 IEEE International Conference on Prognostics and Health Management (ICPHM).
Publikováno v:
Journal of Intelligent Manufacturing. 33:2207-2222
Behind the brilliance of the deep diagnosis models, the issue of distribution discrepancy between source training data and target test data is being gradually concerned for catering to more practical and urgent diagnostic requirements. Consequently,
Autor:
Mingkuan Shi, Chuancang Ding, Hongbo Que, Chengpan Wu, Juanjuan Shi, Changqing Shen, Weiguo Huang, Zhongkui Zhu
Publikováno v:
Measurement. 207:112299