机械设备轴承温度预测模型研究与分析  

Study and Analysis of Bearing Temperature Prediction Model of Mechanical Equipment

作　　者：李腾龙 马卫平 Li Tenglong;Ma Weiping(School of Computer and Artificial Intelligence,Zhengzhou University,Zhengzhou,China;Zhengzhou Research Institute of Mechanical Engineering Co.,Ltd.,Zhengzhou,China)

机构地区：[1]郑州大学计算机与人工智能学院,河南郑州 [2]郑州机械研究所有限公司,河南郑州

出　　处：《科学技术创新》2024年第17期21-24,共4页Scientific and Technological Innovation

摘　　要：本文对工业设备异常温度变化报警模型做了调研汇总,机器学习算法在温度预测与故障诊断过程中发挥了不可忽视的作用。基于高速轴承运行过程的温度样本做分析,比较了线性回归模型和GM(1,1)模型的预测误差,发现GM(1,1)模型在机械设备运转过程中的温度变化预测较线性回归模型更准确,模型精度达到99.59%。本文对机械设备部件运行过程中温度异常变化预警有一定参考意义。This paper makes a survey and summary of the abnormal temperature change alarm model of industrial equipment,and the machine learning algorithm plays an important role in the process of temperature prediction and fault diagnosis.Based on the analysis of the temperature sample of high-speed bearing operation process,comparing the prediction error of linear regression model and GM(1,1)model,we found that the temperature change prediction of GM(1,1)model during the operation of mechanical equipment is more accurate than that of the linear regression model,and the model accuracy reached 99.59%.This paper has some reference significance for the early warning of abnormal temperature change in the operation process of mechanical equipment components.

关 键 词：机械设备 灰色预测 线性回归 异常高温预测 

分 类 号：TH133.3[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]