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A variance change point estimation method based on intelligent ensemble model for quality fluctuation analysis

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  • Sheng Hu
  • Liping Zhao
  • Yiyong Yao
  • Rushan Dou

Abstract

For multivariable production process, knowing the first time of process really changes (change point) will help to accelerate the location of assignable causes and make measures for process adjustment. So effective estimating the change point is an important way to analyse the quality fluctuation of process. In the present study, an intelligent ensemble model for quality fluctuation analysis is proposed to estimate the variance change point in multivariable process. With the method, the process is decomposed based on moving window analysis, then different types of kernel functions are combined together to form the multi-kernel support vector machine model, which has combined the feature mapping capability of each basic kernel in the new feature space. The particle swarm optimisation is considered to search the optimised multi-kernel parameters. After that, each sub-characteristic is regarded as a pattern to be recognised to determine the change point by using the optimised intelligent ensemble model. Finally, a case study is conducted to evaluate the performance of proposed approach. It reveals that the method could estimate the time of variance change point in continuous production process accurately.

Suggested Citation

  • Sheng Hu & Liping Zhao & Yiyong Yao & Rushan Dou, 2016. "A variance change point estimation method based on intelligent ensemble model for quality fluctuation analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 54(19), pages 5783-5797, October.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:19:p:5783-5797
    DOI: 10.1080/00207543.2016.1178862
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Yang Hui & Xuesong Mei & Gedong Jiang & Fei Zhao & Ziwei Ma & Tao Tao, 2022. "Assembly quality evaluation for linear axis of machine tool using data-driven modeling approach," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 753-769, March.
    2. Yang Hui & Xuesong Mei & Gedong Jiang & Fei Zhao & Pengcheng Shen, 2020. "Assembly consistency improvement of straightness error of the linear axis based on the consistency degree and GA-MSVM-I-KM," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1429-1441, August.

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