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Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes

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  • Du, Shichang
  • Lv, Jun

Abstract

Though traditional control charts have been widely used as effective tools in statistical process control (SPC), they are not applicable in many industrial applications where the process variables are highly auto-correlated. In this study, one new minimal Euclidean distance (MED) based monitoring approach is proposed for enhancing the monitoring mean shifts of auto-correlated processes. Support vector regression (SVR) is used to predict the values of a variable in time series. Through calculating minimal Euclidean distance (MED) values over time series, a novel MED chart is developed for monitoring mean shifts, and it can provide a comprehensive and quantitative assessment for the current process state. The performance of the proposed MED control chart is evaluated based on average run length (ARL). Simulation experiments are conducted and one industrial case is illustrated to validate the effectiveness of the developed MED control chart. The analysis results indicate that the developed MED control chart is more effective than other control charts for small process mean shifts in auto-correlated processes, and it can be used as a promising tool for SPC.

Suggested Citation

  • Du, Shichang & Lv, Jun, 2013. "Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes," International Journal of Production Economics, Elsevier, vol. 141(1), pages 377-387.
    • Handle: RePEc:eee:proeco:v:141:y:2013:i:1:p:377-387
    DOI: 10.1016/j.ijpe.2012.09.002
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    References listed on IDEAS

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    Cited by:

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    2. Xueliang Zhou & Pingyu Jiang & Xianxiang Wang, 2018. "Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 51-67, January.
    3. Xueliang Zhou & Pingyu Jiang, 2017. "Variation source identification for deep hole boring process of cutting-hard workpiece based on multi-source information fusion using evidence theory," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 255-270, February.
    4. Iziy Azamsadat & Sadeghpour Gildeh Bahram & Monabbati Ehsan, 2017. "Comparison Between the Economic-Statistical Design of Double and Triple Sampling X¯\bar{X} Control Charts," Stochastics and Quality Control, De Gruyter, vol. 32(1), pages 49-61, June.
    5. Ketai He & Min Zhang & Ling Zuo & Theyab Alhwiti & Fadel M. Megahed, 2017. "Enhancing the monitoring of 3D scanned manufactured parts through projections and spatiotemporal control charts," Journal of Intelligent Manufacturing, Springer, vol. 28(4), pages 899-911, April.
    6. Ho, Linda Lee & Aparisi, Francisco, 2016. "ATTRIVAR: Optimized control charts to monitor process mean with lower operational cost," International Journal of Production Economics, Elsevier, vol. 182(C), pages 472-483.
    7. Khoo, Michael B.C. & Teoh, W.L. & Castagliola, Philippe & Lee, M.H., 2013. "Optimal designs of the double sampling X¯ chart with estimated parameters," International Journal of Production Economics, Elsevier, vol. 144(1), pages 345-357.
    8. Lei, Xue & MacKenzie, Cameron A., 2020. "Distinguishing between common cause variation and special cause variation in a manufacturing system: A simulation of decision making for different types of variation," International Journal of Production Economics, Elsevier, vol. 220(C).
    9. Leoni, Roberto Campos & Costa, Antonio Fernando Branco & Machado, Marcela Aparecida Guerreiro, 2015. "The effect of the autocorrelation on the performance of the T2 chart," European Journal of Operational Research, Elsevier, vol. 247(1), pages 155-165.

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