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Machine Learning Control Charts for Monitoring Serially Correlated Data

In: Control Charts and Machine Learning for Anomaly Detection in Manufacturing

Author

Listed:
  • Xiulin Xie

    (University of Florida)

  • Peihua Qiu

    (University of Florida)

Abstract

Some control charts based on machine learning approaches have been developed recently in the statistical process control (SPC) literature. These charts are usually designed for monitoring processes with independent observations at different observation times. In practice, however, serial data correlation almost always exists in the observed data of a temporal process. It has been well demonstrated in the SPC literature that control charts designed for monitoring independent data would not be reliable to use in applications with serially correlated data. In this chapter, we suggest using certain existing machine learning control charts together with a recursive data de-correlation procedure. It is shown that the performance of these charts can be substantially improved for monitoring serially correlated processes after data de-correlation.

Suggested Citation

  • Xiulin Xie & Peihua Qiu, 2022. "Machine Learning Control Charts for Monitoring Serially Correlated Data," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 131-147, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-030-83819-5_6
    DOI: 10.1007/978-3-030-83819-5_6
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