Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Haopeng Liang"'
Publikováno v:
Space: Science & Technology, Vol 3 (2023)
This paper reports a numerical research on MEMS (microelectromechanical system) micronozzles through multiphysics coupling simulation along with design optimization based on simulation results. The micronozzle, which is a core component of the electr
Externí odkaz:
https://doaj.org/article/6d30c2b1822e4ea7ac159e1b6b0d6c28
Autor:
Haopeng Liang, Xiaoqiang Zhao
Publikováno v:
IEEE Access, Vol 9, Pp 31078-31091 (2021)
As the rolling bearing is the most important part of rotating machinery, its fault diagnosis has been a research hotspot. In order to diagnose the faults of rolling bearing under different noisy environments and different load domains, a new method n
Externí odkaz:
https://doaj.org/article/a2e33fe5ccc4460bba1fd9b82ab3e50a
Publikováno v:
Aerospace, Vol 9, Iss 7, p 360 (2022)
This paper proposes a novel fully nonlinear refined beam element for pre-twisted structures undergoing large deformation and finite untwisting. The present model is constructed in the twisted basis to account for the effects of geometrical nonlineari
Externí odkaz:
https://doaj.org/article/7398ca0a39ef4ba7a9f333432b41a5f3
Publikováno v:
IEEE Sensors Journal. 23:8973-8988
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 71:1-16
Publikováno v:
Measurement. 188:110397
In industrial systems, the vibration signals of rolling bearings are influenced by changing operating conditions and strong environmental noise, therefore they are often characterized by high complexity. The multi-scale deep learning method can achie
Autor:
Haopeng Liang
Publikováno v:
Frontiers of Mechatronical Engineering. 2:89
Because rolling bearings have been working in an environment with complex and variable working conditions and large noise interference for a long time, the bearing fault diagnosis method has a poor diagnostic effect under variable working conditions.