A New Hybrid Ensemble Deep Learning Model for Train Axle Temperature Short Term Forecasting

Chengqing Yu,Guangxi Yan,Yu Bai

doi:10.3390/machines9120312

Abstract

The axle temperature is an index factor of the train operating conditions. The axle temperature forecasting technology is very meaningful in condition monitoring and fault diagnosis to realize early warning and to prevent accidents. In this study, a data-driven hybrid approach consisting of three steps is utilized for the prediction of locomotive axle temperatures. In stage I, the Complementary empirical mode decomposition (CEEMD) method is applied for preprocessing of datasets. In stage II, the Bi-directional long short-term memory (BILSTM) will be conducted for the prediction of subseries. In stage III, the Particle swarm optimization and gravitational search algorithm (PSOGSA) can optimize and ensemble the weights of the objective function, and combine them to achieve the final forecasting. Each part of the combined structure contributes its functions to achieve better prediction accuracy than single models, the verification processes of which are conducted in the three measured datasets for forecasting experiments. The comparative experiments are chosen to test the performance of the proposed model. A sensitive analysis of the hybrid model is also conducted to test its robustness and stability. The results prove that the proposed model can obtain the best prediction results with fewer errors between the comparative models and effectively represent the changing trend in axle temperature.

Highlights

The reliability and efficient operation of the trains has a vital influence on the railway systems because of the continuous increase of railway transport demand
As the first application in axle temperature forecasting, Bi-directional long short-term memory (BILSTM) is tested in experiments with other predictors, which are Long Short-Term Memory (LSTM), DBN, ENN, Back Propagation Neural Network (BPNN), MLP, ARIMA, and ARMA models, including the aspects of neural networks, deep learning, and regression methods
To validate the superiorities of the decomposition methods, the research lists the results of the hybrid Empirical mode decomposition (EMD)-BILSTM, ensemble empirical mode decomposition (EEMD)-BILSTM, and Complementary empirical mode decomposition (CEEMD)-BILSTM models

Summary

Introduction

The reliability and efficient operation of the trains has a vital influence on the railway systems because of the continuous increase of railway transport demand. The approaches applied for railway vehicle safety monitoring have attracted much attention to the developing trend of modern railways [1]. As an effective indicator to reflect the condition of the axles, the research on the axle and bearing temperature monitoring and fault diagnosis can effectively ensure the safety of locomotives and can improve the management level of the railway for significant economic benefits [2]. Liu [8] presented an axle temperature monitoring system with an onboard switched Ethernet, connecting temperature sensors in the axle boxes for the fault diagnosis of high-speed trains. The abovementioned temperature detecting method could obtain the real-time monitoring of the axle temperature, but these methods cannot predict the changing trend of the temperatures, which is more helpful to conduct preventive measures and to avoid unnecessary loss of equipment maintenance. It is meaningful to apply effective data-driven approaches to the axle temperatures for real-time status detection and prediction

Objectives

Results

Discussion

Conclusion