间歇过程多变量统计过程控制的理论与方法

Theories and Methods of Multivariate Statistical Process Control for Batch Process

统计过程控制技术作为一种用统计分析方法保证产品质量和生产稳定性的手段，在现代工业生产中的应用日益广泛。阃歇生产过程因其过程变量的时间相关性和变量之间大多存在强非线性关系的特点，采用传统的统计过程控制方法难以满足其对产品高质量的要求。通过多元投影的方法压缩过程变量的维数，在较低维的主元空间对过程进行监控。可以较好的解决上述矛盾。针对间歇过程运行的特点，分析了线性和非线性多元统计过程控制技术的理论和方法。

As an effective method of improving quality and maintaining consistent, Statistical Process Control （SPC） has wide applications in modem industry. However, in a batch process we can only get massive amounts of process data which are highly correlated with each other in a non-linear relationship. These features make the traditional SPC methods have limited applications for high quality in batch process. These limitations are addressed through the application of multivariate projection techniques. The aim of these approaches is to reduce the dimensionality of the correlated process data and then perform the monitoring in principal component space. The work presents an analysis of linear and non-linear multivariate statistical process control techniques and approaches considering the characteristics of the batch process.