基于核主成分分析的热轧带钢头部拉窄分析

Cause analysis of the head width narrowing of hot rolled strips based on the kernel principal component analysis

将核主成分分析方法引入热轧生产过程的监控与诊断中,根据平方预测误差统计量进行生产过程监控,然后利用数据重构和优化的邻域选取策略相结合的方法求出各工艺参数对平方预测误差统计量的作用,分析引起过程异常的主要工艺参数,最后利用仿真和热轧带钢实际生产数据进行实验.结果表明:基于核主成分分析的平方预测误差统计量能较准确诊断过程的异常,并可以找出引起异常的原因,为调整生产过程提供方法支撑,防止次品的出现.

A method of production quality monitoring and diagnosis based on the kernel principal component analysis was introduced in the hot rolled strip process.The squared prediction error（SPE） statistic was used in process monitoring.The diagnosis criterion could express the influential importance to SPE,which was computed by the data construction method and the optimal neighbor selection strategy.Finally,simulation data and actual production data were used for model validation.The result shows that the SPE statistic based on the kernel principal component analysis can detect the abnormality and track the causes of faults effectively for adjusting the production process to prevent from substandard products.