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A Support Vector Machine Based Multi-kernel Method for Change Point Estimation on Control Chart

Despite the abnormal patterns recognition and mean shift size estimation of control chart signals could provide some evidence for statistical process diagnostics, it do not reveal the real time of the process changes, which is essential for identifying assignable causes and ultimately ensure stability of process. In this paper, a support vector machine based multi-kernel (MK-SVM) method for change point estimation on control chart is proposed. For this purpose, the moving window analysis is introduced to decompose the whole process sequence features into multiple time sub-sequences, and different types of kernel functions are combined together by using kernel method, which is mapped into a new feature space to form the multi-kernel function of SVM. Then each characteristic of the sub-sequences is regarded as a determined pattern to be recognized through the proposed model. We use the cross-validation method to search the optimized parameters of MK-SVM. Multiple sets of experiments are used to verify this method. Finally, a case study about the coating process of production lines is conducted to evaluate the performance of the proposed approach, results reveal that the proposed scheme is able to effectively estimating the time of change-point and outperform the commonly used approaches.