Research on key factors of quality management and control in equipment maintenance based on genetic-neural-network algorithm

Dashan Huang; Le Qian; Yiming Hu

doi:10.1117/12.3051555

18 November 2024 Research on key factors of quality management and control in equipment maintenance based on genetic-neural-network algorithm

Dashan Huang, Le Qian, Yiming Hu

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 1340306 (2024) https://doi.org/10.1117/12.3051555
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

To solve the problems of quality management and control in equipment maintenance operation management, such as the carry out of quality management is not in time, and the implementation is not thorough enough, and the effectiveness is not significant enough, the key factors of quality management and control are determined, assessed and analyzed. The numerical function between quality management and control method and equipment maintenance operation management factor is established based on neural-network, the parameters of input layer, hidden layer, and output layer of neural-network are trained and optimized based on genetic algorithm. Hence, the assess model on key factor of quality management and control in equipment maintenance is accomplished based on genetic-neural-network algorithm. Sample set is generated based on contribution rate and interaction coefficient, and then the assess model is tested and analyzed using the sample set, the simulation results show the effectiveness and efficiency of the assess model with genetic-neural-network algorithm.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dashan Huang, Le Qian, and Yiming Hu "Research on key factors of quality management and control in equipment maintenance based on genetic-neural-network algorithm", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 1340306 (18 November 2024); https://doi.org/10.1117/12.3051555

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