Machine learning based parameter-free adaptive EWMA control chart to monitor process dispersion.

ABSTRACT: Conventional control charts track changes in the process by using predefined process parameters. Conversely, during online monitoring, adaptive control charts modify the process parameters. To improve the process dispersion monitoring in various operational environments, this study presents an adaptive exponentially weighted moving average (AEWMA) control chart based on support vector regression (SVR). This study investigates the efficacy of different kernels such as linear, polynomial, and radial basis functions (RBF) within the SVR framework. By adapting the smoothing constant to the shift's size in process dispersion, the suggested SVR-based AEWMA control chart makes better use of the strengths of the RBF kernel to identify shifts in the process dispersion. To demonstrate the method's e

SUBMITTER: Noor-Ul-Amin M 

PROVIDER: S-EPMC11682192 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature