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A spatial rank–based multivariate EWMA chart for monitoring process shape matrices.

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Title: A spatial rank–based multivariate EWMA chart for monitoring process shape matrices.
Authors: Huwang, Longcheen1 (AUTHOR) huwang@stat.nthu.edu.tw, Lin, Li‐Wei1 (AUTHOR), Yu, Cheng‐Ting1 (AUTHOR)
Source: Quality & Reliability Engineering International. Oct2019, Vol. 35 Issue 6, p1716-1734. 19p. 2 Charts, 7 Graphs.
Subjects: Multivariate analysis, Quality control charts, Covariance matrices, Matrices (Mathematics), Manufacturing processes, Geometric shapes
Abstract: In this article, we propose a nonparametric EWMA control chart for monitoring the shape matrix of a multivariate process based on a spatial rank test and the exponentially weighted moving average scheme. The proposed control chart is essentially developed using an estimated spatial rank covariance matrix to test the shape matrix of the covariance matrix of multivariate distributions with heavy tails. Based on our simulation studies, the proposed control chart outperforms the only existing nonparametric control chart in many practical out‐of‐control scenarios for monitoring the shape matrix of the covariance matrix of many multivariate processes. Further, we point out the weaknesses of both the nonparametric EWMA control charts for monitoring the shape matrix of multivariate processes in real applications and propose one possible method to overcome these weaknesses. We also use an example from a white wine production process to demonstrate the applicability and implementation of the proposed control chart. [ABSTRACT FROM AUTHOR]
Copyright of Quality & Reliability Engineering International is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubTypeId: academicJournal
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  Data: <searchLink fieldCode="AR" term="%22Huwang%2C+Longcheen%22">Huwang, Longcheen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> huwang@stat.nthu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Lin%2C+Li‐Wei%22">Lin, Li‐Wei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Cheng‐Ting%22">Yu, Cheng‐Ting</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Quality+%26+Reliability+Engineering+International%22">Quality & Reliability Engineering International</searchLink>. Oct2019, Vol. 35 Issue 6, p1716-1734. 19p. 2 Charts, 7 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+control+charts%22">Quality control charts</searchLink><br /><searchLink fieldCode="DE" term="%22Covariance+matrices%22">Covariance matrices</searchLink><br /><searchLink fieldCode="DE" term="%22Matrices+%28Mathematics%29%22">Matrices (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+processes%22">Manufacturing processes</searchLink><br /><searchLink fieldCode="DE" term="%22Geometric+shapes%22">Geometric shapes</searchLink>
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  Data: In this article, we propose a nonparametric EWMA control chart for monitoring the shape matrix of a multivariate process based on a spatial rank test and the exponentially weighted moving average scheme. The proposed control chart is essentially developed using an estimated spatial rank covariance matrix to test the shape matrix of the covariance matrix of multivariate distributions with heavy tails. Based on our simulation studies, the proposed control chart outperforms the only existing nonparametric control chart in many practical out‐of‐control scenarios for monitoring the shape matrix of the covariance matrix of many multivariate processes. Further, we point out the weaknesses of both the nonparametric EWMA control charts for monitoring the shape matrix of multivariate processes in real applications and propose one possible method to overcome these weaknesses. We also use an example from a white wine production process to demonstrate the applicability and implementation of the proposed control chart. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Quality & Reliability Engineering International is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/qre.2471
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 19
        StartPage: 1716
    Subjects:
      – SubjectFull: Multivariate analysis
        Type: general
      – SubjectFull: Quality control charts
        Type: general
      – SubjectFull: Covariance matrices
        Type: general
      – SubjectFull: Matrices (Mathematics)
        Type: general
      – SubjectFull: Manufacturing processes
        Type: general
      – SubjectFull: Geometric shapes
        Type: general
    Titles:
      – TitleFull: A spatial rank–based multivariate EWMA chart for monitoring process shape matrices.
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            NameFull: Huwang, Longcheen
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            NameFull: Lin, Li‐Wei
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            NameFull: Yu, Cheng‐Ting
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          Dates:
            – D: 01
              M: 10
              Text: Oct2019
              Type: published
              Y: 2019
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              Value: 35
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            – TitleFull: Quality & Reliability Engineering International
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