Prediction of Key Parameters of Wheelset Based on LSTM Neural Network
As a key component of rail vehicle operation, the running condition of the wheelset significantly affects the operational safety of track vehicles. The wheel diameter, flange thickness, and flange height are key dimensional parameters of the wheelset, which directly influence the correct position of...
Main Authors: | Duo Ye, Jing Wen, Shubin Zheng, Qianwen Zhong, Wanrong Pei, Hongde Jia, Chuanping Zhou, Youping Gong |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-10-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/13/21/11935 |
Similar Items
-
Modeling of the Wear Process of a Locomotive Wheelset and Rail During Sliding in a Curve
by: A. M. Afanasov, et al.
Published: (2023-03-01) -
Effect of Structural Flexibility of Wheelset/Track on Rail Wear
by: Bingguang Wen, et al.
Published: (2023-05-01) -
Deep Subdomain Transfer Learning with Spatial Attention ConvLSTM Network for Fault Diagnosis of Wheelset Bearing in High-Speed Trains
by: Jiujian Wang, et al.
Published: (2023-02-01) -
Design of instrumented wheelset for measuring wheel-rail interaction forces
by: Milan Bižić, et al.
Published: (2023-10-01) -
Joint Maintenance Strategy Optimization for Railway Bogie Wheelset
by: Huixian Zhang, et al.
Published: (2022-07-01)