An effective combined multivariate control chart based on support vector data description

  • Beixin Xia
  • Zheng Jian
  • Ningrong TaoEmail author
Original Research


Conventional multivariate control charts usually focus on a specific process shifts range (small or large), and they cannot get the knowledge of manufacturing process through the learning of in-control data and be effective over the whole range of mean shifts, due to the characteristics of their own structures. In this paper, an effective combined multivariate control chart (named CDD chart, i.e., combined D-MCUSUM and D chart) with an adaptive control limit is proposed to improve the overall detection ability of monitoring techniques in multivariate statistical process control. Besides, this paper also provides a basic methodology for designing the adaptive control limit and recommended values of some key parameters (e.g. window size) for a better application. Based on these, a bivariate simulation experiment is conducted to evaluate the performance of the proposed control chart. Simulation results show that the CDD chart offers a better overall performance, compared with regular control charts (e.g. MCUSUM). In addition, a real industrial case also illustrates the effectiveness of the proposed control chart in applications.


Multivariate statistical process control Multivariate cumulative sum chart Support vector data description Average run length 



This project is supported by National Natural Science Foundation of China (Grant no. 71401098  and 71501125).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of ManagementShanghai UniversityShanghaiChina
  2. 2.School of Mechatronics Engineering and AutomationShanghai UniversityShanghaiChina
  3. 3.College of EngineeringShanghai Ocean UniversityShanghaiChina

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