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High-dimensional process monitoring and change point detection using embedding distributions in reproducing kernel Hilbert space

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  • Shuai Huang
  • Zhenyu Kong
  • Wenzhen Huang

Abstract

High-dimensional process monitoring has become ubiquitous in many domains, which creates tremendous challenges for conventional process monitoring methods. This article proposes a novel Reproducing Kernel Hilbert Space (RKHS)-based control chart that can be applied to high-dimensional processes with sophisticated process distributions to detect a wide range of process changes beyond the ones that are detected by traditional statistical process control methods. Through extensive experiments on both simulated and real-world processes and various kinds of process change patterns, it is shown that the RKHS-based control chart leads to improved statistical stability, fault detection power, and robustness to non-normality as compared with existing methods such as T2 and MEWMA control charts.

Suggested Citation

  • Shuai Huang & Zhenyu Kong & Wenzhen Huang, 2014. "High-dimensional process monitoring and change point detection using embedding distributions in reproducing kernel Hilbert space," IISE Transactions, Taylor & Francis Journals, vol. 46(10), pages 999-1016, October.
    • Handle: RePEc:taf:uiiexx:v:46:y:2014:i:10:p:999-1016
    DOI: 10.1080/0740817X.2013.855848
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    Cited by:

    1. Zhangyue Shi & Abdullah Al Mamun & Chen Kan & Wenmeng Tian & Chenang Liu, 2023. "An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1815-1831, April.
    2. Lianjie Shu & Jinyu Fan, 2018. "A distribution‐free control chart for monitoring high‐dimensional processes based on interpoint distances," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(4), pages 317-330, June.

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