A Multi-Factor Driven Model for Locomotive Axle Temperature Prediction Based on Multi-Stage Feature Engineering and Deep Learning Framework

Recently, with the increasing scale of the volume of freight transport and the number of passengers, the study of railway vehicle fault diagnosis and condition management is becoming more significant than ever. The axle temperature plays a significant role in the locomotive operating condition asses...

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Veröffentlicht in:	Machines (Basel) Jg. 10; H. 9; S. 759
Hauptverfasser:	Yan, Guangxi, Bai, Yu, Yu, Chengqing, Yu, Chengming
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.09.2022
Schlagworte:	Accuracy Algorithms Analysis Artificial intelligence axle temperature forecasting Condition monitoring Datasets Decision making Deep learning Fault diagnosis Feature extraction Feature selection Forecasting Freight transportation Heuristic Locomotives Machine learning Mathematical models Methods Model accuracy Modelling multi-factor driven model Neural networks Optimization Railroad accidents & safety Railway engineering Redundancy reinforcement learning Sensors Shafts (machine elements) Shipment of goods Time series Traffic Vehicles Weather forecasting
ISSN:	2075-1702, 2075-1702
Online-Zugang:	Volltext
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