On autoregressive model selection for the exponentially weighted moving average control chart of residuals in monitoring the mean of autocorrelated processes
On autoregressive model selection for the exponentially weighted moving average control chart of residuals in monitoring the mean of autocorrelated processes
复制标题
监测自相关过程均值的残差指数加权移动平均控制图的自回归模型选择
DOI：
10.1002/qre.2701
复制
发表时间：
2020-07
影响因子：
2.3
通讯作者：
Xiao Yan
中科院分区：
文献类型：
--
作者：
Li Yaping;Pan Ershun;Xiao Yan
随着自动化技术的发展，数据能够以高频方式收集，这很容易导致自相关现象。残差控制图已被用作监测自相关过程的一种良好方法。残差常常……
With the development of automation technologies, data can be collected in a high frequency, easily causing autocorrelation phenomena. Control charts of residuals have been used as a good way to monitor autocorrelated processes. The residuals have been often computed based on autoregressive (AR) models whose building needs much experience. Data have been assumed to be first‐order autocorrelated, and first‐order autoregressive (AR(1)) models have been employed to obtain residuals. But for a pth‐order autocorrelated process, how the AR(1) model affects the performance of the control chart of residuals remains unknown. In this paper, the control chart of exponentially weighted moving average of residuals (EWMA‐R) is used to monitor the pth‐order autocorrelated process. Taking the mean and standard deviation of run length as performance indicators, two types of EWMA‐R control charts, with their residuals obtained from the pth‐order autoregressive AR(p) and AR(1) models, respectively, are compared. The results of the numerical experiment show that for detecting small mean shifts, EWMA‐R control charts based on AR(1) models outperform ones based on AR(p) models, whereas for detecting large shifts, they are sometimes slightly worse. A practical application is used to give a recommendation that a large number of samples are necessary for determining an EWMA‐R control chart before using it.
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影响因子：
2.3
作者：
Fu‐Kwun Wang;Xiao-Bin Cheng
通讯作者：
Fu‐Kwun Wang;Xiao-Bin Cheng
DOI：
10.1007/978-3-030-15334-2_11
发表时间：
2019-09
期刊：
Analog Circuits and Signal Processing
影响因子：
--
作者：
Muhammad Yasin;Jeyavijayan Rajendran;O. Sinanoglu
通讯作者：
Muhammad Yasin;Jeyavijayan Rajendran;O. Sinanoglu
影响因子：
2
作者：
N. Narisetty
通讯作者：
N. Narisetty
影响因子：
2.5
作者：
Chao-Wen Lu;M. R. Reynolds
通讯作者：
Chao-Wen Lu;M. R. Reynolds
影响因子：
2.5
作者：
E. Yashchin
通讯作者：
E. Yashchin