基于混合KPLS-FDA的过程监控和质量预报方法

提出一种基于核偏最小二乘(KPLS)与费舍尔判别分析(FDA)相结合的过程监控和质量预报方法—–混合 KPLS-FDA 方法.首先,利用 KPLS 提取过程数据的非线性特征,使用 FDA 建立 KPLS 的内部模型;然后,求出满足最大分离度的核 Fisher 特征向量和判别向量来实现状态监测,若系统运行正常,则根据 KPLS 回归模型预报产品的质量,否则利用 Fisher 相似度系数确定故障类型;最后,通过轧钢过程的仿真研究验证了混合 KPLS-FDA 方法的有效性.

Process monitoring and quality prediction method based on hybrid KPLS-FDA

A process monitoring and quality prediction method based on combining kernel partial least squares (KPLS)
with Fisher discriminant analysis (FDA), hybrid KPLS-FDA, is proposed. Firstly, the nonlinear feature of process data is
extracted by using KPLS, the internal model of KPLS is established by using FDA, and the optimal feature vector and the
discriminant vector which satisfies maximal separation degree are obtained for condition monitoring. If the process is under
normal condition, the regression model of KPLS is further used for quality prediction. Otherwise, the similar degree in the
fault feature direction is used for fault diagnosis. Finally, the simulation research for steel rolling process is performed to
show its accuracy and effectiveness in fault diagnosis and quality prediction.