دورية أكاديمية

Prediction of Key Parameters of Wheelset Based on LSTM Neural Network

التفاصيل البيبلوغرافية
العنوان: Prediction of Key Parameters of Wheelset Based on LSTM Neural Network
المؤلفون: Duo Ye, Jing Wen, Shubin Zheng, Qianwen Zhong, Wanrong Pei, Hongde Jia, Chuanping Zhou, Youping Gong
المصدر: Applied Sciences, Vol 13, Iss 21, p 11935 (2023)
بيانات النشر: MDPI AG, 2023.
سنة النشر: 2023
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: LSTM neural network, wheelset wear, parameter prediction, deep learning, reprofiling plan, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: 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 wheelsets on the track, and the train needs to be continuously monitored during the passenger operation. A prediction model for the key parameters of the wheelset is established based on LSTM (long short-term memory) neural network, and real measured data of wheelsets from the Shanghai Metro vehicles are selected. The predicted results of the model are compared and analyzed, and the results show that the LSTM-based prediction model for key parameters of wheelsets performs well, with the mean absolute percentage errors (MAPEs) for wheel diameter, flange thickness, and flange height being 0.08%, 0.42%, and 0.44%, respectively, for the left wheel and 0.07%, 0.35%, and 0.44%, respectively, for the right wheel. The prediction model for the train wheelset parameters established in this paper has a good prediction accuracy. By predicting the key parameters of the wheelset, the faults and causes of the wheelset can be found and determined, which is helpful for engineers to overhaul the wheelset faults, make maintenance plans, and perform preventive maintenance.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/13/21/11935; https://doaj.org/toc/2076-3417
DOI: 10.3390/app132111935
URL الوصول: https://doaj.org/article/effddfd4f89d435192f2bb26648ded50
رقم الأكسشن: edsdoj.ffddfd4f89d435192f2bb26648ded50
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app132111935