![]() In this paper, we try to make another effort by proposing a novelĬontrol chart that makes use of the restarting mechanism of a CUSUM chartĪnd the related spring length concept. Some nice discussion on robust design of such residual monitoring controlĬharts, the suggested designs can only handle certain special cases Often invalid, resulting in unstable process monitoring. Series model with a given order and the normality assumption are In applications, however, the assumed parametric time Modeling and residual monitoring, where the data are often assumed to be Most existing methods are based on parametric time series ![]() ![]() To this end, there is some existing discussion in the SPC Important to develop control charts specifically for monitoring seriallyĬorrelated data. It has been wellĭemonstrated in the literature that control charts designed for independentĭata are unstable for monitoring serially correlated data. In practice, however, serial data correlationĪlmost always exists in sequential data. SPC charts are designed for cases when process observations are independentĪt different observation times. ![]() Monitoring, disease surveillance and many other applications. Quality control and management in manufacturing industries, environmental Statistical process control (SPC) charts are critically important for Here, a water quality test dataset is taken on two variables, namely pH and total solids, and the proposed AIB charts are used to monitor the mean level of pH in the water. Several water quality tests are available that provide required information about the health of the waterway. An important concern all around the world is the quality and safety of drinking water. Based on detailed run length comparisons, it is found that the proposed AIB charts are uniformly better than the existing AIB charts in terms of expected weighted and relative average run length benchmarks. We use Monte Carlo simulations to compute the run length properties of the control charts. In addition, fast initial response feature is also added to the proposed charts. In this study, we propose new auxiliary information based (AIB) control charts for monitoring the process mean, which include Brownian motion-based C (BC), new BC (NBC), NBC with Crosier C (NBCC), dual NBC (DNBC) and dual NBCC (DNBCC) charts. The CUSUM (C) and dual C (DC) charts are well-known because of their sensitive nature against small-to-moderate shifts that occur in the process parameter(s). The new method is also illustrated using an example about the exchange rates between Indian Rupees and US Dollars. Simulation studies show that the proposed method is effective in various cases considered. The new method integrates the general framework to construct a CUSUM chart based on the generalized likelihood ratio statistic and estimation of a shift size by the exponentially weighted least square regression procedure. In this paper, we suggest an adaptive cumulative sum (CUSUM) chart to handle the drift detection problem with a flexible drift pattern. In reality, however, such specified patterns may not be valid. ![]() But, most existing methods are designed based on the assumption that the related drift is linear or have another specific pattern. In the literature, there have been some existing discussions on this problem. This is related to the drift detection problem in statistical process control. In practice, sequential processes often have gradual changes in their process distributions over time. ![]()
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