An effective multivariate control chart for detecting small mean shifts using support vector data description

Abstract

Conventional multivariate cumulative sum control charts are more sensitive to small shifts than [Formula: see text] control charts, but they cannot get the knowledge of manufacturing process through the learning of in-control data due to the characteristics of their own structures. To address this issue, a modified multivariate cumulative sum control chart based on support vector data description for multivariate statistical process control is proposed in this article, which is named [Formula: see text] control chart. The proposed control chart will have both advantages of the multivariate cumulative sum control charts and the support vector data description algorithm, namely, high sensitivities to small shifts and learning abilities. The recommended values of some key parameters are also given for a better application. Based on these, a bivariate simulation experiment is conducted to evaluate the performance of the [Formula: see text] control chart. A real industrial case illustrates the application of the proposed control chart. The results also show that the [Formula: see text] control chart is more sensitive to small shifts than other traditional control charts (e.g. [Formula: see text] and multivariate cumulative sum) and a D control chart based on support vector data description.

A new control chart to monitor the mean vector of a trivariate process

“…They also discussed the related applications. Xia et al 11 developed D-MCUSUM control chart which is more efficient than the MCUSUM control chart. Reynolds and Chou 12 proposed multivariate control chart to monitor the mean vector and the covariance matrix with variable sampling interval.…”

“…They also discussed the related applications. Xia et al 11 . developed D‐MCUSUM control chart which is more efficient than the MCUSUM control chart.…”

Monitoring combined multivariate process approaching hybrid kernel‐CUSUM analysis

“…The investigation addresses that the latest observation without considering other data is approached to monitor the condition of the multivariate nonparametric process using kernel method. Xia et al 28 proposed a modified multivariate cumulative sum control chart based on support vector data description for multivariate statistical process control. Xia et al 29 proposed a combined multivariate control chart using an adaptive control limit to improve the overall detection ability of monitoring techniques in the multivariate statistical process control.…”

‘Alternated Charting Statistic Modified Group Runs’ (ACS‐MGR) control chart for monitoring mean vectors of bivariate and trivariate processes

“…It is numerically illustrated that in zero state and in steady state, MV‐UGR‐M is much efficient than MV‐Syn‐M, MV‐GR‐M, and MV‐MGR‐M charts. Xia et al 11 developed an effective chart to monitormean vector. This proposed DCUSUM control chart performs better as compared with MCUSUM control chart.…”