One of the basic assumptions for traditional univariate and multivariate control charts is that the data are independent in time. For the latter, in many cases, the data are serially dependent (autocorrelated) and cross-correlated because of, for example, frequent sampling and process dynamics. It is well known that the autocorrelation affects the false alarm rate and the shift-detection ability of the traditional univariate control charts. However, how the false alarm rate and the shiftdetection ability of the Hotelling T-2 control chart are affected by various autocorrelation and cross- correlation structures for different magnitudes of shifts in the process mean is not fully explored in the literature. In this article, the performance of the Hotelling T-2 control chart for different shift sizes and various autocorrelation and cross- correlation structures are compared based on the average run length using simulated data. Three different approaches in constructing the Hotelling T-2 chart are studied for two different estimates of the covariance matrix: (i) ignoring the autocorrelation and using the raw data with theoretical upper control limits; (ii) ignoring the autocorrelation and using the raw data with adjusted control limits calculated through Monte Carlo simulations; and (iii) constructing the control chart for the residuals from a multivariate time series model fitted to the raw data. To limit the complexity, we use a first-order vector autoregressive process and focus mainly on bivariate data. (c) 2014 The Authors. Quality and Reliability Engineering International published by John Wiley & Sons Ltd.
- Statistical Process Control (SPC)
- Hotelling T2-chart
- Multivariate data
- Time series modeling