Research on Temperature Prediction for Axles of Rail Vehicle Based on LSTM

Jiang²,

et al. 2022

Processes

This paper reviews the current research status of rolling bearing fault diagnosis technology for railway vehicles. Several domains are covered, including vibration fault diagnosis, acoustic signal fault diagnosis, and temperature prediction diagnosis methods on train rolling bearing test principles and related research. The application scenarios, system diagnosis accuracies, and model structures of various studies in the literature are also compared and analyzed. Furthermore, the main technical points to be improved and the analysis of the possible research directions are proposed, which provide new research ideas for subsequent fault diagnosis methods and system innovation research and development.

Section: Fault Diagnosis Of Temperature Features On Train Bearingsmentioning

confidence: 99%

A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles

Jiang²,

et al. 2022

Processes

“…Fu et al designed a new modeling structure to analyze the gearboxbearing temperature changes by the convolutional neural network (CNN) and the long short-term memory (LSTM) [21]. Yang et al used a new modeling structure to analyze temperature changes during high-speed train operation using the LSTM model, which showed that the forecasting errors were arranged within a reasonable range [22]. The gated recurrent unit (GRU) is also employed for bearing residual life forecasting [23].…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

Machines

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.

“…The comparative results have proved that the forecasting error of BPNN is lower than the GM (1,1) model. Yang et al [11] presented an intelligent forecasting structure based on the Long Short-Term Memory (LSTM) for high-speed trains during operation. In the training process, a mean squared error (MSE) is used as the loss function with batch size 100 and the learning rate 0.0001.…”

Section: Related Workmentioning

confidence: 99%

“…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. In recent years, researchers have put forward many prediction methods in the research field of fault diagnosis [9,10], temperature forecasting [11][12][13], wind speed forecasting [14], power forecasting [15,16], traffic flow prediction [17,18], air pollutant forecasting [19] and so on. Hence, it is meaningful to apply effective data-driven approaches to the axle temperatures for real-time status detection and prediction.…”

Section: Introductionmentioning

confidence: 99%

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

2021

Machines

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.