Reliability Analysis of Naval Guns Based on GAN Data Augmentation
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.
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