Name
Abstract | ||
|---|---|---|
In this paper, the field measured wheel wear data of high-speed trains are studied by variance analysis, and prediction models are developed using NAR and BP neural networks. The results show that the wheel position has a significant effect on the wheel wear, and the position of the carriage has little influence on the wheel wear. The NAR neural network can be used to predict the dynamic change of wheel diameter and therefore to predict the wheel wear of high-speed trains. The wheel diameter data are classified and the range of wheel wear can be predicted by means of training the BP neural network. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.24 | 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) |
Keywords | Field | DocType |
Big data,Wheel wear,Variance analysis,NAR neural network,Prediction | Automotive engineering,Data modeling,Computer science,High speed train,Rail transportation,Train,Artificial neural network | Conference |
ISBN | Citations | PageRank |
978-1-5386-3067-9 | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Fan | 1 | 0 | 0.68 |
| Shuwen Wang | 2 | 0 | 0.68 |
| Caixia Liu | 3 | 13 | 2.29 |
| X. Liu | 4 | 276 | 50.30 |