空军工程大学学报2025，Vol.26Issue(4)：1-10,10.DOI:10.3969/j.issn.2097-1915.2025.04.001
基于深度迁移的新型机械类装备故障数量预测
A Prediction of Number of Faults in New Mechanical Equipment Based on Deep Transfer
摘要
Abstract
In view of the problems that samples are limited in size and difficult in establishing a deep model for predicting the number of faults to evaluate the support performance of new mechanical equipment at the stage of testing and identification,a Score evaluation index is proposed by adopting the"transfer learn-ing"method.Large scale mature equipment data was used to assist in training the new equipment fault prediction model.Starting from the perspectives of samples,features,and models in transfer learning,with a focus on deep model-based transfer,this study conducts research on predicting the number of faults.The example shows that the precision of the fine-tuning based model deep transfer has increased by 46.55％and 164.87％respectively in the root mean square error and Score,while the standard deviation has decreased by 86.71％and 91.41％respectively.Far superior to the prediction methods based on sam-ple and feature in design applications of transfer learning and seven typical comparative models,the data-driven advantages of deep learning are fully utilized.With better performance in prediction accuracy,effec-tiveness,and stability,it is conducive to evaluating the support performance of new equipment and promo-ting the construction of equipment test and evaluation.关键词
迁移学习/深度学习/回归预测/小样本/微调/装备试验鉴定Key words
transfer learning/deep learning/regression prediction/small sample size/fine tuning/equipment test and evaluation分类
信息技术与安全科学引用本文复制引用
张红梅,程湘钧,柳泉,拓明福,唐希浪,徐思宁..基于深度迁移的新型机械类装备故障数量预测[J].空军工程大学学报,2025,26(4):1-10,10.基金项目
国家自然科学基金(72201276) （72201276）
