Life Prediction Model of Machine Tool based on Deep Learning

作     者：HE Jiawei ZHAO Chendi GAO Ruiyu LIU Xuehui WANG Xue HE Jiawei;ZHAO Chendi;GAO Ruiyu;LIU Xuehui;WANG Xue

作者机构：School of Information EngineeringChina University of GeosciencesBeijing 100083China 

出 版 物：《International Journal of Plant Engineering and Management》 (国际设备工程与管理（英文版）)

年 卷 期：2021年第26卷第1期

页      面：1-15页

学科分类：12[管理学] 080503[工学-材料加工工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 080201[工学-机械制造及其自动化] 

主　　题：life prediction model machine tool KL divergence metamorphic relation data enhancement 

摘      要：In view of the shortage of traditional life prediction methods for machine tools,such as low accuracy of life prediction and few samples basis attributes,a life prediction model of machine tools combined with machine tool attributes is *** life prediction model of machine tool adopts KL dispersion distribution theory,uses modal superposition method to carry out machine tool life analysis,calculates the theoretical life of machine tool,and then carries on the simulation,obtains the machine tool life prediction *** with the traditional method of machine tool life prediction,the model is based on the application life fatigue damage model,which superimposes the service times and maintenance cycle of the machine tool,derives the influence factor of machine tool life,and obtains the linear relationship between the influence factor of machine tool life and the life of machine *** influence factor of machine tool life is introduced as the life prediction parameter of machine *** data transformation relationship of HT300 parts is *** original part data is *** effective training set is *** life prediction model of machine tool based on deep learning is *** quantitative analysis of machine tool life is carried *** experiment of machine tool life prediction using training data set proves the validity of the *** test was carried out on the training data set to reflect the robustness of the *** prediction accuracy of the model is further verified by Weibull test.