LSTM-Based Temperature Prediction for Hot-Axles of Locomotives

Luo, Chuanwen; Yang, Dan; Huang, Jin; Deng, Yansha

doi:10.1051/itmconf/20171201013

Cited by 14 publications

(9 citation statements)

References 14 publications

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“…In comparison to the traditional modeling techniques, such as statistical methods, ML algorithms are popular when working with time series data [7,8]. RNNs, such as Long Short-Term Memory (LSTM) [9] and its derivative Gated Recurrent Units (GRU) [10], are dominant ML algorithms for modeling temporal correlations from a dataset [7,8,[11][12][13]. This is because of their ability to learn long-range dependencies in sequential data better than conventional RNNs.…”

Section: Related Workmentioning

confidence: 99%

“…The paper shows that an LSTM model outperforms existing baselines including the Feedforward Neural Networks (FNNs) with hand-engineered features. In [11], an LSTM is used to predict the temperature of the axle bearing of a railway. The authors trained an LSTM model with the historical temperature data along with other physical properties, such as pressure, velocity of the train, etc.…”

Section: Related Workmentioning

confidence: 99%

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Modeling and Simulation of a Spacecraft Payload Hardware Using Machine Learning Techniques

et al. 2020

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Space systems are complex and consist of multiple subsystems. Research and development teams of such complex systems are usually distributed among various institutions and space agencies. This affects the quality of the On-board Software (OBSW) since testing it without having all required subsystems at the software development site can be troublesome. In this paper, we present a data-driven method which can be used to synthesize parts of a system or even an entire system as a black-box model. We exploit the data collected from the real hardware to derive a model using a Machine Learning (ML) algorithm. The proposed model can easily be distributed among development teams and is dedicated to emulate the system for testing the OBSW.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Modeling and Simulation of a Spacecraft Payload Hardware Using Machine Learning Techniques

et al. 2020

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“…In the work done by Ma et al [13], it was found that the velocity, the carrying capability and the ambient temperature affect the axle temperature, while the traction has less effect. Luo et al [14] proposed a data-driven approach based on long short-term memory (LSTM) to predict the sensor temperature for axle bearings. Besides, the prediction based on the back propagation (BP) neural network and LSTM network was conducted by researchers [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Luo et al [14] proposed a data-driven approach based on long short-term memory (LSTM) to predict the sensor temperature for axle bearings. Besides, the prediction based on the back propagation (BP) neural network and LSTM network was conducted by researchers [14][15][16]. A neural network predictive control scheme based on adaptive extended particle swarm optimization was also proposed.…”

Section: Introductionmentioning

confidence: 99%

Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Liu

2020

Symmetry

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Predicting the axle temperature states of the high-speed train under operation in advance and evaluating working states of axle bearings is important for improving the safety of train operation and reducing accident risks. The method of monitoring the axle temperature of a train under operation, combined with the neural network prediction method, was applied. A total of 36 sensors were arranged at key positions such as the axle bearings of the train gearbox and the driving end of the traction motor. The positions of the sensors were symmetrical. Axle temperature measurements over 11 days with more than 38,000 km were obtained. The law of the change of the axle temperature in each section was obtained in different environments. The resultant data from the previous 10 days were used to train the neural network model, and a total of 800 samples were randomly selected from eight typical locations for the prediction of axle temperature over the following 3 min. In addition, the results predicted by the neural network method and the GM (1,1) method were compared. The results show that the predicted temperature of the trained neural network model is in good agreement with the experimental temperature, with higher precision than that of the GM (1,1) method, indicating that the proposed method is sufficiently accurate and can be a reliable tool for predicting axle temperature.

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“…In recent years, the rapid development of machine learning algorithms has brought new possibilities for temperature prediction. For instance, Luo et al proposed a long short term memory-based approach to forecast the temperature trend [ 8 , 9 ]. In addition, many other methods based on temperature data have also been proposed [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Chaotic Ensemble of Online Recurrent Extreme Learning Machine for Temperature Prediction of Control Moment Gyroscopes

Liu¹,

Zhang²,

Wei³

et al. 2020

Sensors

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Control moment gyroscopes (CMG) are crucial components in spacecrafts. Since the anomaly of bearing temperature of the CMG shows apparent correlation with nearly all critical fault modes, temperature prediction is of great importance for health management of CMGs. However, due to the complicity of thermal environment on orbit, the temperature signal of the CMG has strong intrinsic nonlinearity and chaotic characteristics. Therefore, it is crucial to study temperature prediction under the framework of chaos time series theory. There are also several other challenges including poor data quality, large individual differences and difficulty in processing streaming data. To overcome these issues, we propose a new method named Chaotic Ensemble of Online Recurrent Extreme Learning Machine (CE-ORELM) for temperature prediction of control moment gyroscopes. By means of the CE-ORELM model, this proposed method is capable of dynamic prediction of temperature. The performance of the method was tested by real temperature data acquired from actual CMGs. Experimental results show that this method has high prediction accuracy and strong adaptability to the on-orbital temperature data with sudden variations. These superiorities indicate that the proposed method can be used for temperature prediction of control moment gyroscopes.

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LSTM-Based Temperature Prediction for Hot-Axles of Locomotives

Cited by 14 publications

References 14 publications

Modeling and Simulation of a Spacecraft Payload Hardware Using Machine Learning Techniques

Modeling and Simulation of a Spacecraft Payload Hardware Using Machine Learning Techniques

Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Chaotic Ensemble of Online Recurrent Extreme Learning Machine for Temperature Prediction of Control Moment Gyroscopes

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