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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Bibliographic Details
Published in:Machines (Basel) Vol. 10; no. 9; p. 759
Main Authors: Yan, Guangxi, Bai, Yu, Yu, Chengqing, Yu, Chengming
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2022
Subjects:
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 Access:Get full text
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Summary: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 assessment that sudden temperature changes may lead to potential accidents. To realize accurate real-time condition monitoring and fault diagnosis, a new multi-data-driven model based on reinforcement learning and deep learning is proposed in this paper. The whole modeling process contains three steps: In step 1, the feature crossing and reinforcement learning methods are applied to select the suitable features that could efficiently shorten the redundancy of the input. In step 2, the stack denoising autoencoder is employed to extract deep fluctuation information in the features after the reinforcement learning. In step 3, the bidirectional gated recurrent unit algorithm is utilized to accomplish the forecasting model and achieve the final results. These parts of the integrated modeling structure contributed to increased forecasting accuracy than single models. By analyzing the forecasting results of three different data series, it could be summarized that: (1) The proposed two-stage feature selection method and feature extraction method could greatly optimize the input for the predictor and form the optimal axle temperature forecasting model. (2) The proposed hybrid model can achieve satisfactory forecasting results which are better than the contrast algorithms proposed by other researchers.
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ISSN:2075-1702
2075-1702
DOI:10.3390/machines10090759