火力与指挥控制2024，Vol.49Issue(4)：38-43,50,7.DOI:10.3969/j.issn.1002-0640.2024.04.006
基于生成对抗网络数据增强的舰炮可靠性分析
Reliability Analysis of Naval Guns Based on GAN Data Augmentation
摘要
Abstract
The lack of fault data of naval guns makes it extremely difficult to analyze the reliability of products.In order to solve the problems of lacking fault data,the generative adversarial network(GAN)is used to augment the fault data.The reliability analysis model of naval guns with GAN data augmen-tation deep neural network is established.The comparison is carried out with indicators obtained from the reliability analysis of original data.The results show that the exponent distribution fitting accuracy improves by 5.40%and Weibull distribution fitting accuracy improves by 11.90%respectively by the expanded samples with GAN data augmentation.Compared with the original data,there is notable im-provement.The methods and ideas are provided for the reliability analysis of fault data of naval guns.关键词
舰炮故障/小样本/GAN/数据增强/可靠性Key words
naval gun failure/small sample/GAN/data augmentation/reliability分类
军事科技引用本文复制引用
聂磊,杨浩明,尹业寒,董正琼,周向东..基于生成对抗网络数据增强的舰炮可靠性分析[J].火力与指挥控制,2024,49(4):38-43,50,7.基金项目
国家自然科学基金(51975191) （51975191）
襄阳湖北工业大学产业研究院基金资助项目(XYYJ2022B01) （XYYJ2022B01）
