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A Novel Scheme of Control Chart Patterns Recognition in Autocorrelated Processes

Author

Listed:
  • Cang Wu

    (School of Mechanical Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

  • Huijuan Hou

    (School of Mechanical Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

  • Chunli Lei

    (School of Mechanical Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

  • Pan Zhang

    (China School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China)

  • Yongjun Du

    (School of Mechanical Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

Abstract

Control chart pattern recognition (CCPR) can quickly recognize anomalies in charts, making it an important tool for narrowing the search scope of abnormal causes. Most studies assume that the observations are normal, independent and identically distributed (NIID), while the assumption of independence cannot always be satisfied under continuous manufacturing processes. Recent research has considered the existence of autocorrelation, but the recognition rate is overestimated. In this paper, a novel scheme is proposed to recognize control chart patterns (CCPs) in which the inherent noise is autocorrelated. By assuming that the inherent noise follows a first-order autoregressive (AR (1)) process, the one-dimensional convolutional neural network (1DCNN) is applied for extracting features in the proposed scheme, while the grey-wolf-optimizer-based support vector machine (GWOSVM) is employed as a classifier. The simulation results reveal that the proposed scheme can effectively identify seven types of CCPs. The overall accuracy is 89.02% for all the autoregressive coefficients, and the highest accuracy is 99.43% when the autoregressive coefficient is on the interval (−0.3, 0]. Comparative experiments indicate that the proposed scheme has great potential to identify CCPs in autocorrelated processes.

Suggested Citation

  • Cang Wu & Huijuan Hou & Chunli Lei & Pan Zhang & Yongjun Du, 2023. "A Novel Scheme of Control Chart Patterns Recognition in Autocorrelated Processes," Mathematics, MDPI, vol. 11(16), pages 1-16, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3589-:d:1220494
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    References listed on IDEAS

    as
    1. Cang Wu & Fei Liu & Bo Zhu, 2015. "Control chart pattern recognition using an integrated model based on binary-tree support vector machine," International Journal of Production Research, Taylor & Francis Journals, vol. 53(7), pages 2026-2040, April.
    2. Héctor De la Torre Gutiérrez & Duc Truong Pham, 2018. "Identification of patterns in control charts for processes with statistically correlated noise," International Journal of Production Research, Taylor & Francis Journals, vol. 56(4), pages 1504-1520, February.
    3. Ethel García & Rita Peñabaena-Niebles & Maria Jubiz-Diaz & Angie Perez-Tafur, 2022. "Concurrent Control Chart Pattern Recognition: A Systematic Review," Mathematics, MDPI, vol. 10(6), pages 1-31, March.
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