No abstract

Aimed at the complex data correlation among multivariate quality characteristics of products, inability to obey the assumptions of traditional control methods, low data volume in small and medium batch production, and inaccurate parameter estimation in process control, an AEWMA-t control method based on kernel distance data is proposed. Firstly, based on the support vector data description algorithm, the hypersphere is trained by normal samples. Then the kernel distance from the sample to the center of the hypersphere is calculated and the kernel distance is normally converted. Finally, the process is controlled with AEWMA-t control method. Case analysis shows that, compared with the traditional multivariate control methods, this method has good ability to detect anomalies in the mean deviation interval of each process, and is not restricted by the distribution and size of the data samples.