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A model‐free approach to reduce the effect of autocorrelation on statistical process control charts
Author(s) -
Ma Xi,
Zhang Laibin,
Hu Jinqiu,
Palazoglu Ahmet
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3070
Subject(s) - autocorrelation , control chart , statistical process control , chart , shewhart individuals control chart , computer science , benchmark (surveying) , statistics , process (computing) , data mining , ewma chart , mathematics , geodesy , geography , operating system
The presence of autocorrelation in time series data significantly affects the performance of statistical process control charts. For process data collected in real time, consecutive observations result in serial dependence that violates the assumption of sample independence. This paper focuses on the improvement of statistical process control (SPC) charts of individual observations when they are designed to monitor stationary processes in the presence of data autocorrelation. Three widely used SPC tools, Shewhart chart, Hotelling's T 2 chart, and PCA‐based SPC charts, are chosen to demonstrate the proposed approach. There has been a modified version of SPC chart shown to be effective in accommodating the autocorrelation behavior, relying on a model‐based estimation of chart parameters. Process model estimation, however, is a challenging task, especially for complex systems. In this paper, a model‐free monitoring approach is proposed for SPC charts of individual observations, aimed at reducing the effect of autocorrelation on chart performance. It relies on a skipping strategy that creates sample bins with little or no autocorrelation. A single skipping chart and a combined skipping chart (CSC) are established, and a parameter sensitivity study is performed through simulations. In the case studies, the advantages of the CSC are noted as being a model‐free approach and having a performance consistent with that of the benchmark modified chart.