A Framework for Diagnosing the Out-of-Control Signals in Multivariate Process Using Optimized Support Vector Machines

Multivariate statistical process control is the continuation and development of unitary statistical process control. Most multivariate statistical quality control charts are usually used (in manufacturing and service industries) to determine whether a process is performing as intended or if there ar...

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Bibliographic Details
Published inMathematical problems in engineering Vol. 2013; no. 2013; pp. 1 - 9
Main Authors Liao, Zhi-qiang, Wei, Zheng-yuan, Hu, Sheng, Li, Tai-fu
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2013
Hindawi Limited
Subjects
Artificial intelligence
Classification
Control charts
Data mining
Engineering
Genetic algorithms
Manufacturing
Multivariate analysis
Neural networks
Process controls
Quality control
Service industries
Statistical process control
Stream flow
Support vector machines
Total quality
Variables
China
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Summary:Multivariate statistical process control is the continuation and development of unitary statistical process control. Most multivariate statistical quality control charts are usually used (in manufacturing and service industries) to determine whether a process is performing as intended or if there are some unnatural causes of variation upon an overall statistics. Once the control chart detects out-of-control signals, one difficulty encountered with multivariate control charts is the interpretation of an out-of-control signal. That is, we have to determine whether one or more or a combination of variables is responsible for the abnormal signal. A novel approach for diagnosing the out-of-control signals in the multivariate process is described in this paper. The proposed methodology uses the optimized support vector machines (support vector machine classification based on genetic algorithm) to recognize set of subclasses of multivariate abnormal patters, identify the responsible variable(s) on the occurrence of abnormal pattern. Multiple sets of experiments are used to verify this model. The performance of the proposed approach demonstrates that this model can accurately classify the source(s) of out-of-control signal and even outperforms the conventional multivariate control scheme.
ISSN:1024-123X
1563-5147
DOI:10.1155/2013/494626