Variance Shifts Identification Model of Bivariate Process Based on LS-SVM Pattern Recognizer

Autor:	Jing Bu, Zhi-Qiang Cheng, Yi-Zhong Ma
Rok vydání:	2011
Předmět:	Statistics and Probability Analysis of covariance Multivariate statistics Chart Covariance matrix Modeling and Simulation Statistics Pattern recognition (psychology) Regression analysis Bivariate analysis Statistical process control Mathematics
Zdroj:	Communications in Statistics - Simulation and Computation. 40:274-284
ISSN:	1532-4141 0361-0918
DOI:	10.1080/03610918.2010.535625
Popis:	Multivariate Statistical Process Control (MSPC) techniques are effective tools for detecting the abnormalities of multivariate process variation. MSPC techniques are based on overall statistics; this has caused the difficulties in interpretation of the alarm signal, that is, MSPC charts do not provide the necessary information about which process variables (or subset of them) are responsible for the signal, and this task is left up to the quality engineers in production field. This article proposes a model based on LS-SVM pattern recognizer to diagnose the bivariate process abnormality in covariance matrix. The main property of this model is a supplement of MSPC |S| chart to identify the variable(s) which is (are) responsible for the process abnormality when |S| chart issue a warning signal. Through simulation experiment, the performance of the model is evaluated by accuracy rate of pattern recognition. The results indicate that the proposed model is an effective method to interpret the root causes of the...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::df1c1c147f5377ed4600571d3ff4f9fe https://doi.org/10.1080/03610918.2010.535625 Zobrazit plný text záznamu