Noor-Ul-Amin Muhammad, Kazmi Muhammad Waqas, Alkhalaf Salem, Abdel-Khalek S, Nabi Muhammad
Department of Statistics, COMSATS University Islamabad-Lahore Campus, Lahore, Pakistan.
Department of Computer Engineering, College of Computer, Qassim University, Buraydah, Saudi Arabia.
Sci Rep. 2024 Dec 28;14(1):31271. doi: 10.1038/s41598-024-82699-8.
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 effectiveness, real-life data is used in a practical application, highlighting the adaptability and reliability of the SVR-based AEWMA control chart for monitoring process dispersion. The code and supplementary data set file may be found at ( https://github.com/muhammadwaqaskazmi/ARL-SDRL-Codes ).
传统控制图通过使用预定义的过程参数来跟踪过程中的变化。相反，在在线监测期间，自适应控制图会修改过程参数。为了改进在各种操作环境中的过程离散度监测，本研究提出了一种基于支持向量回归（SVR）的自适应指数加权移动平均（AEWMA）控制图。本研究调查了SVR框架内不同核函数（如线性、多项式和径向基函数（RBF））的有效性。通过使平滑常数适应过程离散度中偏移的大小，所提出的基于SVR的AEWMA控制图能更好地利用RBF核函数的优势来识别过程离散度中的偏移。为了证明该方法的有效性，在实际应用中使用了实际数据，突出了基于SVR的AEWMA控制图在监测过程离散度方面的适应性和可靠性。代码和补充数据集文件可在（https://github.com/muhammadwaqaskazmi/ARL - SDRL - Codes）找到。
