New Single Variables Control Charts Based On The Double Ewma Statistics

In Statistical Process Control (SPC) monitoring situations, there is a tendency for both the process mean and process variability to shift simultaneously. Traditionally, two separate control charts, each for the mean and variance are used concurrently to monitor the process mean and process variance. However, in many real life process monitoring situations, a simultaneous control of the process mean and process variance is necessary. This has motivated us to develop single DEWMA (called Double Exponentially Weighted Moving Average) charts which are capable of monitoring simultaneous shifts in both the process mean and process variance, when the underlying distribution of the process is normal. The DEWMA statistics are based on the approach of performing exponential smoothing twice on the original statistics of the underlying process. The objective of this study is to propose three single DEWMA charts, namely the DEWMA-Max (called the DEWMA maximum), Max-DEWMA (called the maximum DEWMA) and SS-DEWMA (called the sum of squares of DEWMA) charts

New Single Variables Control Charts Based On The Double Ewma Statistics

In Statistical Process Control (SPC) monitoring situations, there is a tendency for both the process mean and process variability to shift simultaneously. Traditionally, two separate control charts, each for the mean and variance are used concurrently to monitor the process mean and process variance. However, in many real life process monitoring situations, a simultaneous control of the process mean and process variance is necessary. This has motivated us to develop single DEWMA (called Double Exponentially Weighted Moving Average) charts which are capable of monitoring simultaneous shifts in both the process mean and process variance, when the underlying distribution of the process is normal. The DEWMA statistics are based on the approach of performing exponential smoothing twice on the original statistics of the underlying process. The objective of this study is to propose three single DEWMA charts, namely the DEWMA-Max (called the DEWMA maximum), Max-DEWMA (called the maximum DEWMA) and SS-DEWMA (called the sum of squares of DEWMA) charts

Comparison of automatic monitoring systems in automatic forecasting

Forecasting Techniques;mathematische statistiek

Cumulative sum quality control charts design and applications

Includes bibliographical references (pages 165-169).Classical Statistical Process Control Charts are essential in Statistical Control exercises and thus constantly obtained attention for quality improvements. However, the establishment of control charts requires large-sample data (say, no less than I 000 data points). On the other hand, we notice that the small-sample based Grey System Theory Approach is well-established and applied in many areas: social, economic, industrial, military and scientific research fields. In this research, the short time trend curve in terms of GM( I, I) model will be merged into Shewhart and CU SUM two-sided version control charts and establish Grey Predictive Shewhart Control chart and Grey Predictive CUSUM control chart. On the other hand the GM(2, I) model is briefly checked its of how accurate it could be as compared to GM( I, 1) model in control charts. Industrial process data collected from TBF Packaging Machine Company in Taiwan was analyzed in terms of these new developments as an illustrative example for grey quality control charts

Characterization of Model-Based Detectors for CPS Sensor Faults/Attacks

A vector-valued model-based cumulative sum (CUSUM) procedure is proposed for identifying faulty/falsified sensor measurements. First, given the system dynamics, we derive tools for tuning the CUSUM procedure in the fault/attack free case to fulfill a desired detection performance (in terms of false alarm rate). We use the widely-used chi-squared fault/attack detection procedure as a benchmark to compare the performance of the CUSUM. In particular, we characterize the state degradation that a class of attacks can induce to the system while enforcing that the detectors (CUSUM and chi-squared) do not raise alarms. In doing so, we find the upper bound of state degradation that is possible by an undetected attacker. We quantify the advantage of using a dynamic detector (CUSUM), which leverages the history of the state, over a static detector (chi-squared) which uses a single measurement at a time. Simulations of a chemical reactor with heat exchanger are presented to illustrate the performance of our tools.Comment: Submitted to IEEE Transactions on Control Systems Technolog

Minimax Optimality of CUSUM for an Autoregressive Model

Different change point models for AR(1) processes are reviewed. For some models, the change is in the distribution conditional on earlier observations. For others the change is in the unconditional distribution. Some models include an observation before the first possible change time — others not. Earlier and new CUSUM type methods are given and minimax optimality is examined. For the conditional model with an observation before the possible change there are sharp results of optimality in the literature. The unconditional model with possible change at (or before) the first observation is of interest for applications. We examined this case and derived new variants of four earlier suggestions. By numerical methods and Monte Carlo simulations it was demonstrated that the new variants dominate the original ones. However, none of the methods is uniformly minimax optimal.Autoregressive; Change point; Monitoring; Online detection