DocumentCode :
75977
Title :
SVM and ANN Application to Multivariate Pattern Recognition Using Scatter Data
Author :
Chinas, Pamela ; Lopez, Ismael ; Vazquez, Jose Antonio ; Osorio, Roman ; Lefranc, Gaston
Author_Institution :
Unidad Saltillo, Centro de Investig. y de Estudios Av. del Inst. Politec. Nac. (CINVESTAV), Saltillo, Mexico
Volume :
13
Issue :
5
fYear :
2015
fDate :
May-15
Firstpage :
1633
Lastpage :
1639
Abstract :
Several methods of Statistical Process Control (SPC) are used to analyze process measurements with the purpose to detect faults that affect the process stability. SPC has a major drawback because it indicates the presence of faults without explaining which ones and where are the faults. In practical applications, SPC just analyses univariate signals limiting the study of multiple measures. Nowadays, novel methods have been developed for fault analysis based on pattern recognition in control charts. However, the majority of these studies follow a univariate approach. This article proposes a multivariate pattern recognition approach using machine learning algorithms in conjunction with a scatter diagram as the proposed method. In particular the aim of this approach is to monitor quality characteristics of a product in a multivariate environment considering states in control and out of control without the constraints of statistical conditions with the possibility of its application in real time. Results using Support Vector Machines (SVM) and the FuzzyARTMAP neural network showed that multivariate patterns can be recognized successfully in 81% of the cases.
Keywords :
control charts; fuzzy neural nets; pattern recognition; statistical analysis; statistical process control; support vector machines; ANN; SPC; SVM; control charts; fault detection; fuzzy ARTMAP neural network; machine learning algorithms; multivariate pattern recognition approach; process stability; scatter data; scatter diagram; statistical process control; support vector machines; Control charts; Media; Monitoring; Pattern recognition; Principal component analysis; Support vector machines; Time series analysis; Fuzzy ARTMAP; Multivariate patterns; PCA; SVM; univariate patterns;
fLanguage :
English
Journal_Title :
Latin America Transactions, IEEE (Revista IEEE America Latina)
Publisher :
ieee
ISSN :
1548-0992
Type :
jour
DOI :
10.1109/TLA.2015.7112025
Filename :
7112025
Link To Document :
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