GCG: Graph Convolutional network and gated recurrent unit method for high-speed train axle temperature forecasting
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
GCG: Graph Convolutional network and gated recurrent unit method for high-speed train axle temperature forecasting
Authors
Keywords
Axle temperature forecast, Graph convolutional neural network, Gated recurrent units, High-speed train
Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 163, Issue -, Pages 108102
Publisher
Elsevier BV
Online
2021-06-18
DOI
10.1016/j.ymssp.2021.108102
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Multivariate/minor fault diagnosis with severity level based on Bayesian decision theory and multidimensional RBC
- (2021) Ying Zheng et al. JOURNAL OF PROCESS CONTROL
- A new deep transfer learning network based on convolutional auto-encoder for mechanical fault diagnosis
- (2021) Quan Qian et al. MEASUREMENT
- Investigation of the Design and Fault Prediction Method for an Abrasive Particle Sensor Used in Wind Turbine Gearbox
- (2020) Le Zhang et al. Energies
- LSTM-based indoor air temperature prediction framework for HVAC systems in smart buildings
- (2020) Fatma Mtibaa et al. NEURAL COMPUTING & APPLICATIONS
- A China Railway Express-Based Model for Designing a Cross-Border Logistics Information Cloud Platform Scheme
- (2020) Qian Huang et al. Applied Sciences-Basel
- Attitude data-based deep hybrid learning architecture for intelligent fault diagnosis of multi-joint industrial robots
- (2020) Jianyu Long et al. JOURNAL OF MANUFACTURING SYSTEMS
- A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting
- (2020) Mohamed Massaoudi et al. ENERGY
- Superposition Graph Neural Network for offshore wind power prediction
- (2020) Mei Yu et al. Future Generation Computer Systems-The International Journal of eScience
- Adaptive stochastic-filter-based failure prediction model for complex repairable systems under uncertainty conditions
- (2020) Peng Yizhen et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Learning performance prediction via convolutional GRU and explainable neural networks in e-learning environments
- (2019) Xizhe Wang et al. COMPUTING
- Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA
- (2019) Pouya Aghelpour et al. THEORETICAL AND APPLIED CLIMATOLOGY
- Evolving Deep Echo State Networks for Intelligent Fault Diagnosis
- (2019) Jianyu Long et al. IEEE Transactions on Industrial Informatics
- Empirical analysis of change metrics for software fault prediction
- (2018) Garvit Rajesh Choudhary et al. COMPUTERS & ELECTRICAL ENGINEERING
- SVR-Based Model to Forecast PV Power Generation under Different Weather Conditions
- (2017) Utpal Das et al. Energies
- LSTM network: a deep learning approach for short-term traffic forecast
- (2017) Zheng Zhao et al. IET Intelligent Transport Systems
- Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques
- (2015) Hazlee Azil Illias et al. PLoS One
- A comparative study of fault density prediction in aspect-oriented systems using MLP, RBF, KNN, RT, DENFIS and SVR models
- (2012) Mahmoud O. Elish ARTIFICIAL INTELLIGENCE REVIEW
- Daily river water temperature forecast model with a k-nearest neighbour approach
- (2011) André St-Hilaire et al. HYDROLOGICAL PROCESSES
- Forecast of NDVI in coniferous areas using temporal ARIMA analysis and climatic data at a regional scale
- (2011) A. Fernández-Manso et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- A symbolic fault-prediction model based on multiobjective particle swarm optimization
- (2010) André B. de Carvalho et al. JOURNAL OF SYSTEMS AND SOFTWARE
- Fault prediction of the nonlinear systems with uncertainty
- (2008) Zhijie Zhou et al. SIMULATION MODELLING PRACTICE AND THEORY
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreDiscover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversation