@Article{icces.2023.09663,
AUTHOR = {Hang Cheng, Jiawen Fei, Jianfeng Wen, Shan-Tung Tu},
TITLE = {Predictive Maintenance of Alkaline Water Electrolysis System for  Hydrogen Production Based on Digital Twin},
JOURNAL = {The International Conference on Computational \& Experimental Engineering and Sciences},
VOLUME = {27},
YEAR = {2023},
NUMBER = {2},
PAGES = {1--1},
URL = {http://www.techscience.com/icces/v27n2/54161},
ISSN = {1933-2815},
ABSTRACT = {Alkaline water electrolysis system for hydrogen production has the characteristics of complex structure, 
fault coupling and state nonlinearity, coupled with the restriction by many factors such as data acquisition 
methods and analysis methods. The operation status cannot be fully characterized through current 
monitoring information. In order to solve the problems in health status assessment in the operation of 
alkaline water electrolysis system, a digital twin-driven predictive maintenance method is put forward to 
achieve the real-time monitoring of operation status and prediction of remaining useful life. In the study, a 
multi-disciplinary simulation model of the alkaline electrolysis system and a physical degradation model of 
the electrolyzer are established. Meanwhile, the data-driven fault diagnosis and the life prediction algorithm 
are constructed by using the deep learning method. Finally, the two are fused by the particle filtering 
algorithm and transfer learning to realize predictive maintenance of alkaline water electrolysis system. 
Results indicate that, in contrast with the single model-based method or the data-driven method, the 
predictive method based on digital twin has higher prediction accuracy, which overcomes the problems of 
inconsistent models and poor adaptability of data algorithms. For the fault diagnosis of the alkaline water 
electrolysis system, the fault diagnosis model is trained based on digital twin simulation data, and then 
transferred to the data collected by actual sensors by the transfer learning method. The diagnosis accuracy 
reaches 90%, indicating that the method is able to relatively better diagnose the fault in the operation of the 
alkaline water electrolysis system for hydrogen production. The predictive maintenance method based on 
digital twin proposed in this paper also provides an effective solution for other complex equipment.},
DOI = {10.32604/icces.2023.09663}
}