Author(s)

Shuai Yue ¹, Yizhi Zhang ¹, Zhen Li ¹, Qingqing Hu ¹

Affiliation(s)

¹ Shijiazhuang Campus, Army Engineering University, Shijiazhuang, China

Corresponding Author

Zhen Li

ABSTRACT

Aiming at the problems of low intelligence and networking in predictive maintenance of military intelligent equipment, and difficulty in physical model modeling, the AI-based military intelligent equipment remote fault prediction and health management system (PHM) implementation framework, key technologies and guarantee decision-making methods are studied.The operating mode of the PHM system is explained in detail, and the software architecture and key technologies of the PHM system are analyzed on this basis. The outstanding features of AI-wide communication, pan-awareness, and self-learning are used to construct the health management of military intelligent equipment that integrates the PHM system architecture. The system realizes data-driven, intelligent and networked guarantee for the health management of military intelligent equipment. This paper provide reference and reference for military intelligent equipment support suitable for complex conditions, reduce operation and maintenance costs, and continuously improve the level of military intelligent equipment support.

KEYWORDS

Artificial Intelligence; Intelligent Equipment; PHM

CITE THIS PAPER

Shuai Yue, Yizhi Zhang, Zhen Li, Qingqing Hu, Research on Military Intelligent Equipment Support Integrating Artificial Intelligence and PHM. Industrial Engineering and Innovation Management (2022) Vol. 5: 59-63. DOI: http://dx.doi.org/10.23977/ieim.2022.051007.

REFERENCES

[1] Qu Ch. Q, (2021) Research on PHM data system architecture for complex equipment. [J]. Computer Measurement and Control, 29(04): 1-4+9. 
[2] Shao Sh. G, (2021) Research on the Development Trend of Intelligent Equipment Support Technology. [J]. Flying Missile, (04): 90-94. 
[3] Xu W.F, (2021) Application of PHM technology in aircraft assembly unit. [J]. Modern Manufacturing Technology and Equipment, 57(03): 142-144. 
[4] Gao M.L, (2021) Research on Urban Rail Transit Vehicle Maintenance Decision-Making Technology Integrating RCM, PHM and Data Mining. [J]. Urban Rail Transit Research, 24(02):64-68.
[5] Jing B, Jiao X. X, (2019) Big data analysis and artificial intelligence application for aircraft PHM. [J]. Journal of Air Force Engineering University (NATURAL SCIENCE EDITION), 2019, 20 (01): 46-54.

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