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

doi:10.3901/JME.2023.11.264

Abstract

Abstract: 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,.

Key words: missing data, gear hobbing, carbon consumption prediction, generative adversarial network

CLC Number:

TH122

YI Qian, LIU Chun, LI Congbo, ZHAO Xikun, YI Shuping. Prediction Method of Hobbing Carbon Consumption Based on Improved Generative Adversarial Imputation Net with Missing Data[J]. Journal of Mechanical Engineering, 2023, 59(11): 264-275.

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