Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Jingtai Wang"'
Publikováno v:
IEEE Access, Vol 9, Pp 11226-11240 (2021)
In the noisy environment, fault characteristics of the composite faults of the wind power gearbox are coupled with each other, which makes the extraction features more difficult. In order to extract the characteristics of composite faults, a new faul
Externí odkaz:
https://doaj.org/article/c897851ed81a4e03b055f154d7983bd3
Publikováno v:
IEEE Access, Vol 7, Pp 29520-29532 (2019)
Minimum entropy deconvolution (MED) is widely used in the gearbox fault diagnosis because it can enhance the energy of the impact signal. However, it is sensitive to single abnormal impulsive oscillation. This is because it takes kurtosis as the obje
Externí odkaz:
https://doaj.org/article/5ccba95870274225adbc23c701d23f13
Autor:
Zhijian Wang, Gaofeng He, Wenhua Du, Jie Zhou, Xiaofeng Han, Jingtai Wang, Huihui He, Xiaoming Guo, Junyuan Wang, Yanfei Kou
Publikováno v:
IEEE Access, Vol 7, Pp 44871-44882 (2019)
The selection of variational mode decomposition (VMD) parameters usually adopts the empirical method, trial-and-error method, or single-objective optimization method. The above-mentioned method cannot achieve the global optimal effect. Therefore, a m
Externí odkaz:
https://doaj.org/article/845ae29d818549f9a88d7970da6ec68e
Autor:
Zhijian Wang, Junyuan Wang, Wenan Cai, Jie Zhou, Wenhua Du, Jingtai Wang, Gaofeng He, Huihui He
Publikováno v:
Complexity, Vol 2019 (2019)
In industrial production, it is highly essential to extract faults in gearbox accurately. Specifically, in a strong noise environment, it is difficult to extract the fault features accurately. LMD (local mean decomposition) is widely used as an adapt
Externí odkaz:
https://doaj.org/article/44b835af037845749697e56540ea6389
Autor:
Zhijian Wang, Likang Zheng, Wenhua Du, Wenan Cai, Jie Zhou, Jingtai Wang, Xiaofeng Han, Gaofeng He
Publikováno v:
Complexity, Vol 2019 (2019)
In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as
Externí odkaz:
https://doaj.org/article/7e6660be0b814d92a261a730594c69a2
Autor:
Jie Zhou, Xiaoming Guo, Zhijian Wang, Wenhua Du, Junyuan Wang, Xiaofeng Han, Jingtai Wang, Gaofeng He, Huihui He, Huiling Xue, Yanfei Kou
Publikováno v:
Entropy, Vol 21, Iss 4, p 400 (2019)
In recent years, a new method of fault diagnosis, named variational mode decomposition (VMD), has been widely used in industrial production, but the decomposition accuracy of VMD is determined by two parameters, which are respectively the decompositi
Externí odkaz:
https://doaj.org/article/915bdd407a254da1b8cef70956b4fa5f
Publikováno v:
IEEE Access, Vol 9, Pp 11226-11240 (2021)
In the noisy environment, fault characteristics of the composite faults of the wind power gearbox are coupled with each other, which makes the extraction features more difficult. In order to extract the characteristics of composite faults, a new faul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::804f51f5683fba085dafd53de45e4843
https://doi.org/10.1109/access.2021.3049789
https://doi.org/10.1109/access.2021.3049789
Autor:
Wenhua Du, Jingtai Wang, Wenan Cai, Zhijian Wang, Xiaofeng Han, Likang Zheng, Gaofeng He, Jie Zhou
Publikováno v:
Complexity, Vol 2019 (2019)
In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fdbccc368e14af69868685b1d0228ca3
https://doi.org/10.1155/2019/6943234
https://doi.org/10.1155/2019/6943234
Publikováno v:
IEEE Access, Vol 7, Pp 29520-29532 (2019)
Minimum entropy deconvolution (MED) is widely used in the gearbox fault diagnosis because it can enhance the energy of the impact signal. However, it is sensitive to single abnormal impulsive oscillation. This is because it takes kurtosis as the obje
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f3f83eafec3690c854c597aef394bd71
https://ieeexplore.ieee.org/document/8648439/
https://ieeexplore.ieee.org/document/8648439/
Autor:
Wenhua Du, Jie Zhou, Junyuan Wang, Yanfei Kou, Gaofeng He, Xiaofeng Han, Zhijian Wang, Xiaoming Guo, Jingtai Wang, Huihui He
Publikováno v:
IEEE Access, Vol 7, Pp 44871-44882 (2019)
The selection of variational mode decomposition (VMD) parameters usually adopts the empirical method, trial-and-error method, or single-objective optimization method. The above-mentioned method cannot achieve the global optimal effect. Therefore, a m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a137e97e6ee5ac0784aa63765695569d
https://doi.org/10.1109/access.2019.2909300
https://doi.org/10.1109/access.2019.2909300