Search

A novel approach for biosurveillance using multiple data streams is presented. The proposed method is computationally simple, has rapid detection ability, and produces few false alarms. The proposed algorithm is applied to three popular statistical process control (SPC) charts: the Shewhart Chart, the EWMA, and the CUSUM. The proposed method collects disease counts from multiple data streams, computes charting statistics, and then compares these to empirical in-control distributions generated using bootstrap methods to decide whether to signal an alarm. As bootstrap methods are used, no assumption is made about the in-control distributions corresponding to a specific parametric distribution—an assumption that is common with most conventional SPC methods. The proposed method relies on p-values and controls the false discovery rate; which distinguishes it from traditional SPC methods. The relatively low false alarm rate is a highlight of the proposed method, as higher false alarm rates are a common problem with conventional SPC charts. Through extensive simulations, the EWMA and CUSUM methods are shown to have superior performance over the widely-used Shewhart charts, with the EWMA having a slight advantage over the CUSUM. The proposed method is applied to the 2011 Escherichiacoli outbreak in Germany. [ABSTRACT FROM AUTHOR]

This paper develops an integrated economic model for the joint optimization of quality control parameters and a preventive maintenance policy using the cumulative sum (CUSUM) control chart and variable sampling interval fixed time sampling policy. To determine the in-control and out-of-control cost for both mean and variance, a Taguchi quadratic loss function and modified linear loss function are used, respectively. Imperfect preventive maintenance and minimal corrective maintenance policies were considered in developing the model, which determines the optimal values for significant process parameters to minimize the total expected cost per unit of time. A numerical example is used to test the model, which is followed by a sensitivity analysis. The integration of CUSUM mean and variance charts with the maintenance actions are proven successful to detect the slightest shift of the process. The findings reveal that among all the cost components, process failure due to external causes and equipment breakdown has a noteworthy attribute to the total costs of the optimized model. It is expected that top managers can utilize the suggested combined model to minimize the costs related to quality loss and maintenance policy and achieve economical advantages as well. [ABSTRACT FROM AUTHOR]

The ordinary CUSUM chart is based on normality assumption. But in real-life phenomenon such as monitoring of lifetime variable, this assumption is not always valid. Hence, a variant of the CUSUM chart, the VCUSUM chart, has been constructed to monitor small shift in a process that is based on a Maxwell distribution. The performance of the chart has been checked by studying the run length properties and it was found to perform better for small shifts as compared to Shewhart type V chart for Maxwell parameter. Finally, a real-life application is presented to monitor the failure rate of a vertical boring machine. [ABSTRACT FROM AUTHOR]

The process personnel always seek the opportunity to improve the processes. One of the essential steps for process improvement is to quickly recognize the starting time or the change point of a process disturbance. The proposed approach combines the CUSUM control chart with the Bayesian estimation technique. We show that the control chart has some information about the change point and this information can be used to make an informative prior. Two Bayes estimators corresponding to the informative and a non informative prior along with MLE, frequentist approach, are considered. Their mean square error of estimators, are compared through a series of simulations. The results show that the Bayes estimator with the informative prior is more accurate and more precise for almost all values of the shift in the process mean compared to Bayes estimator with a non-informative prior and MLE. [ABSTRACT FROM AUTHOR]

Today's competitive market has witnessed a growing interest in improving the reliability of products in both service and industrial operations. A large number of monitoring schemes have been introduced to effectively control the reliability-related quality characteristics. These methods have focused on single-stage processes or considered quality variables which are independent. However, the main feature of multistage processes is the cascade property which needs to be justified for the sake of optimal process monitoring. The problem becomes complicated when the presence of censored observations is pronounced. Therefore, both the effects of influential covariates and censored data must be taken into account while presenting a monitoring scheme. In this paper, the accelerated failure time models are used and two regression-adjusted control schemes based on Cox-Snell residuals are devised. Two different scenarios with censored and non-censored data are considered respectively. The competing control charts are compared in terms of zero-state and steady-state average run length criteria using Markov chain approach. The comparison study reveals that the cumulative sum based monitoring procedure is superior and more effective. It should be noted that the application of the proposed monitoring schemes are not restricted to manufacturing processes and thus service operations such as healthcare systems can benefit from them. [ABSTRACT FROM AUTHOR]

The CUSUM charts have been widely used in statistical process control (SPC) across industries for monitoring process shifts and supporting online measurement and distributed computing. This paper proposes an algorithm for the optimimal design of a CUSUM control chart detecting process shifts in the mean value. The algorithm optimizes the sample size, sampling interval, control limit and reference parameter of the CUSUM chart through minimizing the overall mean value (ML) of a Taguchi’s loss function over the probability distribution of the random process mean shift. A new feature related to the exponential of the sample mean shift is elaborated. Comparative studies reveal that the proposed ML-CUSUM chart is considerably superior to the Shewhart ML- $$\overline{X} $$ chart and the conventional CUSUM chart in terms of the overall loss of ML. [ABSTRACT FROM AUTHOR]