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A distance-based control chart for monitoring multivariate processes using support vector machines

Author

Listed:
  • Shuguang He

    (Tianjin University)

  • Wei Jiang

    (Shanghai Jiao Tong University)

  • Houtao Deng

    (Instacart)

Abstract

Traditional control charts assume a baseline parametric model, against which new observations are compared in order to identify significant departures from the baseline model. To monitor a process without a baseline model, real-time contrasts (RTC) control charts were recently proposed to monitor classification errors when seperarting new observations from limited phase I data using a binary classifier. In contrast to the RTC framework, the distance between an in-control dataset and a dataset of new observations can also be used to measure the shift of the process. In this paper, we propose a distance-based multivariate process control chart using support vector machines (SVM), referred to as D-SVM chart. The SVM classifier provides a continuous score or distance from the boundary for each observation and allows smaller sample sizes than the previously random forest based RTC charts. An extensive experimental study shows that the RTC charts with the SVM scores are more efficient than those with the random forest for detecting changes in high-dimensional processes and/or non-normal processes. A real-life example from a mobile phone assembly process is also considered.

Suggested Citation

  • Shuguang He & Wei Jiang & Houtao Deng, 2018. "A distance-based control chart for monitoring multivariate processes using support vector machines," Annals of Operations Research, Springer, vol. 263(1), pages 191-207, April.
    • Handle: RePEc:spr:annopr:v:263:y:2018:i:1:d:10.1007_s10479-016-2186-4
    DOI: 10.1007/s10479-016-2186-4
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    References listed on IDEAS

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    1. Mohammad Poursaeidi & O. Kundakcioglu, 2014. "Robust support vector machines for multiple instance learning," Annals of Operations Research, Springer, vol. 216(1), pages 205-227, May.
    2. Thuntee Sukchotrat & Seoung Kim & Fugee Tsung, 2010. "One-class classification-based control charts for multivariate process monitoring," IISE Transactions, Taylor & Francis Journals, vol. 42(2), pages 107-120.
    3. Shuchun Wang & Wei Jiang & Kwok-Leung Tsui, 2010. "Adjusted support vector machines based on a new loss function," Annals of Operations Research, Springer, vol. 174(1), pages 83-101, February.
    4. Changliang Zou & Xianghui Ning & Fugee Tsung, 2012. "LASSO-based multivariate linear profile monitoring," Annals of Operations Research, Springer, vol. 192(1), pages 3-19, January.
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    Cited by:

    1. Song, Zhi & Mukherjee, Amitava & Liu, Yanchun & Zhang, Jiujun, 2019. "Optimizing joint location-scale monitoring – An adaptive distribution-free approach with minimal loss of information," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1019-1036.

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