Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Jian-Hua Zhong"'
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-15 (2022)
Abstract To address the issue of not having enough labeled fault data for planetary gearboxes in actual production, this research develops a simulation data-driven deep transfer learning fault diagnosis method that applies fault diagnosis knowledge f
Externí odkaz:
https://doaj.org/article/e4bd4d1deae44762b1468b15e26b8b4f
Autor:
Zhu-Fu Shao, Jian-Hua Zhong, John Howell, Bing Hao, Xi-Wu Luan, Ze-Xuan Liu, Wei-Min Ran, Yun-Feng Zhang, Hong-Qi Yuan, Jing-Jing Liu, Liang-Tian Ni, Guan-Xian Song, Jin-Lin Liu, Wen-Xin Zhang, Bing Zhao
Publikováno v:
Journal of Palaeogeography, Vol 9, Iss 1, Pp 1-19 (2020)
Abstract An earthquake of magnitude M5.7 occurred in Yamutu village, Songyuan City, Jilin Province, NE China (45°16′12″N/124°42′35″E) on May 28, 2018, with a focal depth of 13 km. The epicenter is located at the intersection of the Fuyu/Son
Externí odkaz:
https://doaj.org/article/13dc49654f5049e3815285067aa90630
Publikováno v:
IEEE Access, Vol 7, Pp 773-781 (2019)
In order to reduce operation and maintenance costs, reliability, and quick response capability of multi-fault intelligent diagnosis for the wind turbine system are becoming more important. This paper proposes a rapid data-driven fault diagnostic meth
Externí odkaz:
https://doaj.org/article/e19e329b39d440bbb78beb57303027b5
Publikováno v:
IEEE Access, Vol 6, Pp 63777-63793 (2018)
With the rapid growth of the automotive technology, electronically controlled air suspension has been widely used to improve ride comfort and handling stability of the vehicle by actively modulating the suspension stiffness, vehicle height, and postu
Externí odkaz:
https://doaj.org/article/21cdf83cdaeb4efea38ba60041b15cc7
Publikováno v:
Shock and Vibration, Vol 2016 (2016)
Fault diagnosis is very important to maintain the operation of a gas turbine generator system (GTGS) in power plants, where any abnormal situations will interrupt the electricity supply. The fault diagnosis of the GTGS faces the main challenge that t
Externí odkaz:
https://doaj.org/article/946861fd720e47d182df31b7e854c49e
Publikováno v:
Energies, Vol 10, Iss 10, p 1652 (2017)
Intelligent fault diagnosis of complex machinery is crucial for industries to reduce the maintenance cost and to improve fault prediction performance. Acoustic signal is an ideal source for diagnosis because of its inherent characteristics in terms o
Externí odkaz:
https://doaj.org/article/ea55d35b2b964a7ea7601050586c5100
Publikováno v:
Energies, Vol 9, Iss 6, p 379 (2016)
Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment
Externí odkaz:
https://doaj.org/article/7dff24b1ad76451383181fdab2e9c1fc
Autor:
Zhi-Xin Yang, Jian-Hua Zhong
Publikováno v:
Entropy, Vol 18, Iss 4, p 112 (2016)
Acoustic signals are an ideal source of diagnosis data thanks to their intrinsic non-directional coverage, sensitivity to incipient defects, and insensitivity to structural resonance characteristics. However this makes prevailing signal de-nosing and
Externí odkaz:
https://doaj.org/article/d240dd35022940f4966477641e08adea
Publikováno v:
Sensors, Vol 16, Iss 2, p 185 (2016)
This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detectio
Externí odkaz:
https://doaj.org/article/d09dc1c2beec4b9b9f4962bdb564a8f8
Autor:
Ze-Yi Dai, Yuan-Cun Nie, Zi Hui, Lan-Xin Liu, Zi-Shuo Liu, Jian-Hua Zhong, Jia-Bao Guan, Ji-Ke Wang, Yuan Chen, Ye Zou, Hao-Hu Li, Jian-Hua He
Publikováno v:
Nuclear Science and Techniques. 34
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e099282c6b7a0b1fd2222ec7ab861416
https://doi.org/10.1007/s41365-023-01183-6
https://doi.org/10.1007/s41365-023-01183-6