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Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process

Author

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  • Yuehjen E. Shao

    (Department of Statistics and Information Science, Fu Jen Catholic University, Xinzhuang Dist., New Taipei City 24205, Taiwan)

  • Yu-Ting Hu

    (Department of Statistics and Information Science, Fu Jen Catholic University, Xinzhuang Dist., New Taipei City 24205, Taiwan)

Abstract

A statistical process control (SPC) chart is one of the most important techniques for monitoring a process. Typically, a certain root cause or a disturbance in a process would result in the presence of a systematic control chart pattern (CCP). Consequently, the effective recognition of CCPs has received considerable attention in recent years for their potential use in improving process quality. However, most studies have focused on the recognition of CCPs for SPC applications alone. Specifically, even though numerous studies have addressed the increased use of the SPC and engineering process control (EPC) mechanisms, very little research has discussed the recognition of CCPs for multiple-input multiple-output (MIMO) systems. It is much more difficult to recognize the CCPs of an MIMO system since two or more disturbances are simultaneously involved in the process. The purpose of this study is thus to propose several machine learning (ML) classifiers to overcome the difficulties in recognizing CCPs in MIMO systems. Because of their efficient and fast algorithms and effective classification performance, the considered ML classifiers include an artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and multivariate adaptive regression splines (MARS). Furthermore, one problem may arise due to the existence of embedded mixture CCPs (MCCPs) in MIMO systems. In contrast to using typical process outputs alone in a classifier, this study employs both process outputs and EPC compensation to ensure the effectiveness of CCP recognition. Experimental results reveal that the proposed classifiers are able to effectively recognize MCCPs for MIMO systems.

Suggested Citation

  • Yuehjen E. Shao & Yu-Ting Hu, 2020. "Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process," Mathematics, MDPI, vol. 8(1), pages 1-14, January.
  • Handle: RePEc:gam:jmathe:v:8:y:2020:i:1:p:102-:d:306191
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    References listed on IDEAS

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    1. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
    2. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    3. 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.
    4. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    5. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
    6. Yuehjen E. Shao & Po-Yu Chang & Chi-Jie Lu, 2017. "Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process," Complexity, Hindawi, vol. 2017, pages 1-10, October.
    7. Guangzhou Diao & Liping Zhao & Yiyong Yao, 2015. "A dynamic quality control approach by improving dominant factors based on improved principal component analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 53(14), pages 4287-4303, July.
    8. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 1.
    9. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    10. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
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    Cited by:

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    2. 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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