Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach

2011 ◽  
Vol 61 (4) ◽  
pp. 1123-1134 ◽  
Author(s):  
Shih-Yen Lin ◽  
Ruey-Shiang Guh ◽  
Yeou-Ren Shiue
Keyword(s):  
Support Vector Machine ◽  
Control Chart ◽  
Support Vector ◽  
Control Chart Patterns ◽  
Autocorrelated Data ◽  
Chart Patterns

Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine

2013 ◽  
Vol 64 (1) ◽  
pp. 280-289 ◽  
Author(s):  
Liangjun Xie ◽  
Nong Gu ◽  
Dalong Li ◽  
Zhiqiang Cao ◽  
Min Tan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Control Chart ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Support Vector ◽  
Concurrent Control ◽  
Singular Spectrum ◽  
Control Chart Patterns ◽  
Chart Patterns ◽  
Control Chart Patterns Recognition

scholarly journals Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yuehjen E. Shao ◽  
Po-Yu Chang ◽  
Chi-Jie Lu
Keyword(s):  
Neural Network ◽  
Process Control ◽  
Control Chart ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Two Stage ◽  
Accurate Identification ◽  
Statistical Process ◽  
Control Chart Patterns ◽  
Chart Patterns

The effective controlling and monitoring of an industrial process through the integration of statistical process control (SPC) and engineering process control (EPC) has been widely addressed in recent years. However, because the mixture types of disturbances are often embedded in underlying processes, mixture control chart patterns (MCCPs) are very difficult for an SPC-EPC process to identify. This can result in problems when attempting to determine the underlying root causes of process faults. Additionally, a large number of categories of disturbances may be present in a process, but typical single-stage classifiers have difficulty in identifying large numbers of categories of disturbances in an SPC-EPC process. Therefore, we propose a two-stage neural network (NN) based scheme to enhance the accurate identification rate (AIR) for MCCPs by performing dimension reduction on disturbance categories. The two-stage scheme includes a combination of a NN, support vector machine (SVM), and multivariate adaptive regression splines (MARS). Experimental results reveal that the proposed scheme achieves a satisfactory AIR for identifying MCCPs in an SPC-EPC system.

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

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Min Zhang ◽  
Wenming Cheng
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Control Chart ◽  
Control Charts ◽  
Support Vector ◽  
Shape Features ◽  
Multiclass Support Vector Machine ◽  
Control Chart Pattern ◽  
Chart Patterns

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.

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