关键词: 统计过程控制, 多变量过程, 均值异常, 模式识别, 有向无环图支持向量机

Abstract: Due to the large quantities of mean abnormality mode's type, the general identification tool was difficult to adapt it in multivariate process. Aiming at this problem, an optimizing Directed Acyclic Graph Support Vector Machine (DAG-SVM) was proposed, which had high recognition rate and could generate the topology structure through heuristic approach. According to the average difference measure of mode type in kernel space, the type numbers were sequenced. The corresponding binary support vector machines were extracted from sequence to construct the directed acyclic graph structure, which could.assign the easily classified support vector machines to upper layers of the structure. It alleviated the classification error accumulation effect and made up the fault-tolerant ability insufficient of classes appearing in upper layers. Therefore, relatively higher overall classification accuracy was ensured. The simulation experiment results showed that the proposed optimizing DAG-SVM had comprehensive advantages in mean abnormality mode for recognition accuracy and efficiency than other types of multi-class support vector machines. An identification model for mean abnormalities in multivariate process based on optimizing DAG-SVM was built, and its validity as well as practicality was verified in actual gear production process.

Key words: statistical process control, multivariate process, mean abnormality, pattern recognition, directed acyclic graph support vector machine