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Health Prediction of Integrated Die-Casting Machine Driven by Digital Twin and CNN-LSTM

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DOI: 10.23977/jemm.2024.090304 | Downloads: 9 | Views: 234

Author(s)

Lijun Liu 1, Yongpeng Cao 1, Yalou Gao 1, Kaixing Liu 1, Yiteng Ma 1

Affiliation(s)

1 College of Mechanical and Electrical Engineering, Shaanxi University of Science & Technology, Xi'an, Shaanxi, 710021, China

Corresponding Author

Lijun Liu

ABSTRACT

In order to solve the problem that the health status of the integrated die-casting machine is difficult to control during the operation and maintenance process, a health state prediction method of the integrated die-casting machine driven by the fusion of digital twin and CNN-LSTM was proposed. Firstly, based on the digital twin theory, a digital twin model of condition monitoring of the integrated die-casting machine was constructed to realize the real-time mapping of the real-time status and performance parameters of the integrated die-casting machine and the digital twin. Secondly, based on the CNN-LSTM machine learning algorithm, the life characteristics data of key components of the integrated die-casting machine were mined, and the life prediction model of the key components of the integrated die-casting machine was established, so as to realize the online prediction of the remaining effective life driven by the real-time monitoring data of the twin model. Finally, the effectiveness of the proposed method is verified by constructing an integrated status monitoring and health prediction system for the integrated die-casting machine, which provides a new idea for the intelligent maintenance and management of the integrated die-casting machine.

KEYWORDS

Integrated die-casting machine, Condition monitoring, Life prediction, Digital twin; CNN-LSTM

CITE THIS PAPER

Lijun Liu, Yongpeng Cao, Yalou Gao, Kaixing Liu, Yiteng Ma, Health Prediction of Integrated Die-Casting Machine Driven by Digital Twin and CNN-LSTM. Journal of Engineering Mechanics and Machinery (2024) Vol. 9: 23-37. DOI: http://dx.doi.org/10.23977/jemm.2024.090304.

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