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Monitoring Series of Dependent Observations Using the sXWAM Control Chart for Residuals

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Soft Modeling in Industrial Manufacturing

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 183))

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Abstract

Control charts for monitoring residuals are the main tools for statistical process control of autocorrelated streams of data. X chart for residuals, calculated from a series of individual observations, is probably the most popular, but its statistical characteristics are not satisfactory, especially for charts designed using limited amount of data. In order to improve these characteristics, a new chart for residuals using the concept of weighted model averaging (XWAM chart) was previously proposed by the authors. Unfortunately, the design of the XWAM chart is rather complicated, and it requires significant computational effort. In this paper, we propose its simplification, named sXWAM chart, which is simpler to design, and in some practically important cases, has similar statistical properties.

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Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447, Warszawa, Poland

    Olgierd Hryniewicz & Katarzyna Kaczmarek-Majer

Authors
  1. Olgierd Hryniewicz
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  2. Katarzyna Kaczmarek-Majer
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Corresponding author

Correspondence to Olgierd Hryniewicz .

Editor information

Editors and Affiliations

  1. Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland

    Przemyslaw Grzegorzewski

  2. Faculty of Production Engineering, Institute of Manufacturing Technologies, Warsaw University of Technology, Warsaw, Poland

    Andrzej Kochanski

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Hryniewicz, O., Kaczmarek-Majer, K. (2019). Monitoring Series of Dependent Observations Using the sXWAM Control Chart for Residuals. In: Grzegorzewski, P., Kochanski, A., Kacprzyk, J. (eds) Soft Modeling in Industrial Manufacturing. Studies in Systems, Decision and Control, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-030-03201-2_9

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