| Abstrakt: |
Profile monitoring is an active research area in statistical process control (SPC) because it has many important applications in manufacturing and other industries. Early profile monitoring methods often impose model assumptions that the mean profile function has a parametric form (e.g., linear), profile observations have a parametric distribution (e.g., normal), and within‐profile observations are independent of each other. These assumptions have been lifted in some recent profile monitoring research, making the related methods more reliable to use in various applications. One notoriously challenging task in profile monitoring research is to properly accommodate serial data correlation among profiles observed at different time points, and this task has not been properly addressed in the SPC literature yet. Serial data correlation is common in practice, and it has been well demonstrated in the literature that control charts are unreliable to use if the serial data correlation is ignored. In this paper, we suggest a novel mixed‐effects model for describing serially correlated univariate profile data. Based on this model, a Phase I profile monitoring chart is developed. This chart is flexible in the sense that it does not require any parametric forms for describing the mean profile function and the profile data distribution. It can accommodate both the within‐profile and between‐profile data correlation. Numerical studies show that it works well in different cases. [ABSTRACT FROM AUTHOR] |
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