Support Vector Machines for Control of Multimodal Processes

In recent manufacturing processes, the number of common causes of variation increases with the complexity of processes, leading to different shifts of the in-control process between multiple modes. Such a multimodal process violates the normality assumption, which decreases the efficiency of the commonly used methods and often disables the usage of SPC. This paper investigates the performance of one-class support vector machine (OSVM) in a multimodal setting. We have generated 5-modal synthetic data set with two correlated variables that violate the normality assumption. These methods were compared on the horizontally, vertically, and diagonally shifted out-of-control data. We have found that OSVM outperforms the other two commonly used SPC methods, which demonstrates that its more flexible decision boundary can naturally wrap the data from multimodal processes and can bring benefits to the control of modern complex processes.