南京航空航天大学学报（英文版）2024，Vol.41Issue(2)：147-157,11.DOI:10.16356/j.1005‑1120.2024.02.002
基于终身学习的直升机装配车间物料送达时间预测
Lifelong Learning Based Material Delivery Time Prediction for Helicopter Assembly
马立俊 1阳祥贵 2郭宇 1童周强 2黄少华 1刘道元1
作者信息
- 1. 南京航空航天大学机电学院,南京 210016,中国
- 2. 江西昌河航空工业有限公司,景德镇 333000,中国
- 折叠
摘要
Abstract
The lack of key materials has emerged as one of crucial factors affecting the execution of helicopter assembly production plans.Accurate material delivery time prediction can guide assembly production planning and reduce frequent changes caused by material shortages.A lifelong learning-based model for predicting delivery time of materials is proposed on the basis of internal data sharing within the helicopter factory.During real-time prediction,the model can store new memories quickly and not forget old ones,which is constructed by gated recurrent unit(GRU)network layer,ReLU activation layer,and fully connected layers.To prevent significant precision degradation in real-time prediction,a regularization parameter constraint method is proposed to adjust model parameters.By using this method,the root mean square error(RMSE)in the model's prediction on the target domain data is reduced from 0.032 9 to 0.013 4.The accuracy and applicability of the model for real-time prediction in helicopter assembly is validated by comparing it with methods such as L2 regularization and EWC regularization,using 25 material orders.关键词
直升机装配车间/物料送达预测/终身学习/参数正则化Key words
helicopter assembly/material delivery forecast/lifelong learning/parameter regularization分类
信息技术与安全科学引用本文复制引用
马立俊,阳祥贵,郭宇,童周强,黄少华,刘道元..基于终身学习的直升机装配车间物料送达时间预测[J].南京航空航天大学学报（英文版）,2024,41(2):147-157,11.
