A self-starting statistical control chart methodology for data exhibiting linear trend Brian Matthew McClurg , University of Iowa Follow

[摘要] Traditional quality control charts are designed to monitor and control a quality characteristic for processes with a stable in-control state in which enough data is available to estimate the process parameters prior to a production run. For many processes we desire the ability to monitor a quality characteristic that has an in-control state not stable such as a degradation or deterioration process that exhibits a linear trend as its in-control state. In addition, there are many times when sufficient sampling for in-control parameter estimation is not possible before the production run due to cost or collection time. We therefore desire a self-starting charting scheme that monitors both in-control and out of control linear trends. We present here the needed results so that a process with the in-control linear trend can be charted to detect slope and intercept shifts, when accurate information on in-control parameters is not available. We propose a Q chart scheme, a EWMA Q chart, and a EWMA Q chart with delay parameter d that utilizes results from statistical process control and linear regression. The developed control chart schemes are tested through simulation studies and applied to real data examples.