Support vector machine in statistical process monitoring: a methodological and analytical review

Abstract

In statistical process monitoring, data mining algorithms are applied for control chart pattern recognition (CCPR) not only to detect but also to identify abnormal patterns associated with assignable causes. Recently, the use of support vector machines (SVMs) has achieved remarkable results in statistical process control applications due to its excellent generalization performance. Although there is a lot of research that highlights the superiority of support vector machine over other algorithms used in systems based on data mining control, there are no studies that analyze the design elements of a SVM classifier. Consequently, a comprehensive review, classification and an analysis of information found in the literature are presented in this paper regarding to the SVM classifier. The aim of this research is to provide researchers a starting point to potentiate the performance of the SVM classifier for assuring the best possible classification and improving the detection efficiency. Sixty-one research articles from 2001 to 2015 are critically analyzed based on their methodology following a classification scheme derived from the support vector classification framework. The analysis showed that the feature extraction and selection play a crucial role on the performance of classifiers. Studies revealed that using extracted features or a combination of raw data and feature extraction achieves better classification performances than using only raw data as an input. On the other hand, there is an ample gap to extend the research on internal structure configurations under various types of kernels, multi-classification, and ensemble approaches. Finally, this paper evidences that the application of nature inspired algorithms for kernel parameter selection in auto-correlated SVM-based process monitoring systems remains unexplored.