Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features

Abstract

Control charts have been widely utilized for monitoring process variation in numerous applications. Abnormal patterns exhibited by control charts imply certain potentially assignable causes that may deteriorate the process performance. Most of the previous studies are concerned with the recognition of single abnormal control chart patterns (CCPs). This paper introduces an intelligent hybrid model for recognizing the mixture CCPs that includes three main aspects: feature extraction, classifier, and parameters optimization. In the feature extraction, statistical and shape features of observation data are used in the data input to get the effective data for the classifier. A multiclass support vector machine (MSVM) applies for recognizing the mixture CCPs. Finally, genetic algorithm (GA) is utilized to optimize the MSVM classifier by searching the best values of the parameters of MSVM and kernel function. The performance of the hybrid approach is evaluated by simulation experiments, and simulation results demonstrate that the proposed approach is able to effectively recognize mixture CCPs.

Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods

“…The second approach takes as input some shape features and also statistical features extracted from y (processed data). Based on the literature [7,8,9,10,11,12,13], the shape features considered in this study are:…”

“…This study considers offline classification and our analysis is based on Support Vector Machine (SVM) classifiers [37]. This classifier has been widely applied to process control [8,12,13,14,25]. Briefly, the SVM is a classifier that adjust an optimal hyperplane that provides the separation of the classes with the largest margin.…”

“…Besides these features, a great number of methods are based on shape features [7,9,10,11], and its results have shown that the use of shape features as inputs of the classifier discriminates well the patterns, leading to higher classification rates. Ranaee, Ebrahimzadeh and Ghaderi [8], Zhang and Cheng [12] and Wu, Liu and Zhu15 [13] developed a method composed of statistical and shape features as inputs of a support vector machine (SVM) classifier. In [12], genetic algorithm was applied as an optimization tool to improve the performance of the multiclass SVM classifier.…”

“…Ranaee, Ebrahimzadeh and Ghaderi [8], Zhang and Cheng [12] and Wu, Liu and Zhu15 [13] developed a method composed of statistical and shape features as inputs of a support vector machine (SVM) classifier. In [12], genetic algorithm was applied as an optimization tool to improve the performance of the multiclass SVM classifier. The model presented in [13] uses shape and statistical features as inputs of a binary-class SVM which identifies if the pattern is normal or not and, if the latter case is true, a binary-tree SVM is employed to recognize the pattern involved.…”

Dynamic Risk Analysis in Metro Construction Using Statistical Process Control

“…Some scholars made the improvements and applications for SPC. For example, He et al [18], Zhang and Cheng [19], and Ramadan [20] improved different control charts based on different practical demands. He et al [21,22] proposed, respectively, the risk analysis method and the optimization model for quality control of the proces by combing the SPC method.…”