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
Key words
mechanical equipment/gray prediction/linear regression/abnormal high temperature prediction
维普
万方数据