WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON 1D-CNN AND SWLSTM

WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON 1D-CNN AND SWLSTM

Aiming at the subtle fault features of the wind turbines rolling bearing, the fault signal is nonlinear, non-stationary and contains noise interference, and the fault signal has the characteristics of space and time feature information, a space-time fusion convolutional shared weight long short-term...

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Bibliographic Details
Main Authors: JING DongXing, CHEN YangHui, QUAN Zhe
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Strength 2023-12-01
Series:Jixie qiangdu
Subjects:
Wind power
Fault diagnosis
Rolling bearing
Shared weight long short-term memory network
One-dimensional convolutional neural network
Online Access:http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.06.006
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