Prediction Method of Hobbing Carbon Consumption Based on Improved Generative Adversarial Imputation Net with Missing Data

被引：0

作者：

Yi Q. ^{[1
,2
]}

Liu C. ^{[2
]}

Li C. ^{[1
,2
]}

Zhao X. ^{[1
,2
]}

Yi S. ^{[2
]}

机构：

[1] State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing

[2] College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing

来源：

Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2023年 / 59卷 / 11期

关键词：

carbon consumption prediction; gear hobbing; generative adversarial network; missing data;

D O I：

10.3901/JME.2023.11.264

中图分类号：

学科分类号：

摘要：

Aiming at the problem of low prediction accuracy of carbon consumption prediction model due to the missing data of carbon consumption, a prediction method of carbon consumption based on improved generative adversarial imputation net is proposed. Taking gear hobbing as an example, the carbon consumption characteristics of gear hobbing process are revealed, and the missing mechanism of carbon consumption data in gear hobbing process is analyzed. The loss function of generative adversarial imputation net (GAIN) is constructed by introducing regularization mechanism, and the carbon consumption data imputation method based on regularized generative adversarial imputation net (RGAIN) is proposed. Then, the random forest (RF) algorithm is used to construct a prediction model of hobbing carbon emission, and the dynamic prediction of hobbing carbon consumption is realized. Finally, the proposed method is compared with other data imputation and carbon consumption prediction methods. The results show that the proposed method can effectively reduce the prediction error caused by the missing carbon consumption data of gear hobbing,. © 2023 Editorial Office of Chinese Journal of Mechanical Engineering. All rights reserved.

引用

页码：264 / 275

页数：11

共 22 条

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