The unmanned and intelligent transformation of military equipment has triggered great revolutions in battlefield morphology and combat patterns. Among them, unmanned autonomous vehicles have played an important role in military operations by participating in various tasks such as reconnaissance, target striking, and transportation. Among its structures, the powertrain is a core component of unmanned autonomous vehicles. The operation and maintenance of the powertrain are essential for the equipment's safety and reliability. And the quality of operation and maintenance directly determines the durability of unmanned autonomous vehicles. Therefore, this paper analyzed the realistic requirements of state control of powertrain and summarized technical difficulties as three aspects, including lack of data, lack of calculation, and lack of algorithm evolution. Through the systematic combing of the digital twin concept and the current situation, it is found that the digital twins can play three beneficial roles, including optimizing the layout and selection of physical space sensors, realizing the effective fusion of information space and physical space, and improving generalization and adaptability. An architecture of the digital twin system for unmanned autonomous vehicles powertrain is proposed, in which the key technologies are condensed, such as the overall design technology of the system, multi-dimensional and multi-state data acquisition, and analysis for virtual space mapping, the model construction of the digital twin system for condition monitoring and control, the fault diagnosis and health assessment based on the digital twin, and the decision-making of unmanned autonomous vehicles maintenance based on the health assessment.
Digital Twin System for Condition Monitoring and Control of Unmanned Autonomous Vehicle Powertrain
Mechan. Machine Science
International conference on the Efficiency and Performance Engineering Network ; 2024 ; Qingdao, China May 08, 2024 - May 11, 2024
Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic ; Chapter : 52 ; 571-578
2024-09-04
8 pages
Article/Chapter (Book)
Electronic Resource
English
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