High-Dimensional Nonparametric EWMA Control Chart Based on Empirical Likelihood Test
With the gradual improvement of sensor technology and data acquisition system,a large number of complex high-dimensional data can be collected.Monitor-ing multi-variable and high-dimensional data streams are often a basic requirement of modern manufacturing and quality management departments.However,in the field of high dimensional data monitoring,most of the traditional multivariate control charts are no longer applicable due to the"curse of dimension"and the complicated and unknown distribution of variables.In response to this situation,some researchers have discussed various tests for the mean vector of complex high-dimensional data with unknown distribution.But these tests are rarely applicable to Phase Ⅱ process monitoring.In this paper,we propose an EWMA-type nonparametric monitoring scheme based on high-dimensional empirical likelihood ratio test,which can be used to monitor the mean vector of multi-dimensional and highdimensional processes,and is suitable for subgroup data streams.The proposed control chart is not only easy to implement and interpret,but also the Monte Carlo numerical simulation results show that the proposed control chart can effectively detect the mean shift in symmetric,skewed and heavy-tailed distributions.Finally,the proposed control chart is applied to the semiconductor manufacturing process,and the results show that the proposed method has a good monitoring effect on the semiconductor that has not passed the test.
High-dimensionalnon-parametricmultivariate statistical process con-trolempirical likelihood ratio testcontrol chart
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