A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction
出版年份 2020 全文链接
标题
A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction
作者
关键词
Deep learning, Digital twin, Prognostics, Proton exchange membrane fuel cell, Remaining useful life
出版物
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
Volume 46, Issue 2, Pages 2555-2564
出版商
Elsevier BV
发表日期
2020-11-06
DOI
10.1016/j.ijhydene.2020.10.108
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Clean energy production by PEM fuel cells on tourist ships: A time-dependent analysis
- (2020) M. Rivarolo et al. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
- Data-driven fault diagnosis for PEMFC systems of hybrid tram based on deep learning
- (2020) Xuexia Zhang et al. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
- Degradation prediction of PEM fuel cell based on artificial intelligence
- (2020) L. Vichard et al. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
- Experimental analysis of performance degradation of 3-cell PEMFC stack under dynamic load cycle
- (2020) Jaesu Han et al. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
- Design tool for estimating metal hydride storage system characteristics for light-duty hydrogen fuel cell vehicles
- (2020) Kriston P. Brooks et al. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
- Review on health-conscious energy management strategies for fuel cell hybrid electric vehicles: Degradation models and strategies
- (2019) Meiling Yue et al. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
- Size and power exchange optimization of a grid-connected diesel generator-photovoltaic-fuel cell hybrid energy system considering reliability, cost and renewability
- (2019) Mahdi Gharibi et al. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
- MsM: A microservice middleware for smart WSN-based IoT application
- (2019) Ayoub Benayache et al. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
- Smart City Digital Twin–Enabled Energy Management: Toward Real-Time Urban Building Energy Benchmarking
- (2019) Abigail Francisco et al. JOURNAL OF MANAGEMENT IN ENGINEERING
- Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues
- (2019) Yuqian Lu et al. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
- Digital twin driven prognostics and health management for complex equipment
- (2018) Fei Tao et al. CIRP ANNALS-MANUFACTURING TECHNOLOGY
- Digital twin-based smart production management and control framework for the complex product assembly shop-floor
- (2018) Cunbo Zhuang et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Maximum conversion efficiency of hydrogen fuel cells
- (2018) Y. Haseli INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
- Remaining useful life estimation in prognostics using deep convolution neural networks
- (2018) Xiang Li et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Fuel cell health prognosis using Unscented Kalman Filter: Postal fuel cell electric vehicles case study
- (2018) Kui Chen et al. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
- Digital Twin for rotating machinery fault diagnosis in smart manufacturing
- (2018) Jinjiang Wang et al. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
- Digital twin-driven product design, manufacturing and service with big data
- (2017) Fei Tao et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Proton exchange membrane fuel cell ageing forecasting algorithm based on Echo State Network
- (2017) Simon Morando et al. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
- Data-based short-term prognostics for proton exchange membrane fuel cells
- (2017) Hao Liu et al. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
- Degradations analysis and aging modeling for health assessment and prognostics of PEMFC
- (2016) Marine Jouin et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Proton exchange membrane fuel cell degradation prediction based on Adaptive Neuro-Fuzzy Inference Systems
- (2014) R.E. Silva et al. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
- Reengineering Aircraft Structural Life Prediction Using a Digital Twin
- (2011) Eric J. Tuegel et al. International Journal of Aerospace Engineering
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