Monitoring nonlinear profile data using support vector regression method

Chung-I Li, Jeh-Nan Pan, Chun Han Liao

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In today's manufacturing industries, if the quality characteristic of a product or a process is assumed to be represented by a functional relationship between the response variable and one or more explanatory variables, then the data generated from such a relationship are called profile data. Generally speaking, the functional relationship of the profile data rarely occurs in linear form, and the real data usually do not follow normal distribution. Thus, in this paper, the functional relationship of profile data is described via a nonparametric regression model and a nonparametric exponentially weighted moving average (EWMA) control chart is developed for detecting the process shifts for nonlinear profile data in the Phase II monitoring. We first fit the nonlinear profile data via a support vector regression model and use the fitted values to calculate the five metrics. Then, the nonparametric EWMA control chart with the five metrics can be constructed accordingly. Moreover, a simulation study is conducted to evaluate the detecting performance of the new control chart under various process shifts using the out-of-control average run length. Finally, a realistic nonlinear profile example is used to demonstrate the usefulness of our proposed nonparametric EWMA control chart and its monitoring schemes. It is expected that the proposed nonparametric EWMA control chart can enhance the monitoring efficiency for nonlinear profile data in the phase II study.

Original languageEnglish
Pages (from-to)127-135
Number of pages9
JournalQuality and Reliability Engineering International
Volume35
Issue number1
DOIs
Publication statusPublished - 2019 Feb 1

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Monitoring
Normal distribution
Control charts
Regression method
Support vector regression
Industry
Exponentially weighted moving average
Regression model

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research

Cite this

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Monitoring nonlinear profile data using support vector regression method. / Li, Chung-I; Pan, Jeh-Nan; Liao, Chun Han.

In: Quality and Reliability Engineering International, Vol. 35, No. 1, 01.02.2019, p. 127-135.

Research output: Contribution to journalArticle

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