Journal of Quality Engineering and Management

Abstract Traditional control diagrams are based on the basic assumption that process data are sequentially independent of each other and have a normal distribution. However, in many cases in the real world, including in chemical and continuous processes, this assumption does not exist and there is a kind of autocorrelation between the data collected from the process. The use of traditional control charts in self-correlated processes is unreliable and increases erroneous warnings. One of the methods developed to control autocorrelation processes is to identify the structure of process time series and use residual values ​​to control process. In this paper, a model based on adaptive neural-fuzzy systems to identify time series structure and predict design use. May be. Is in AR (2). Also, the residual control diagrams based on this system for the self-return data of degree 2 and for (EWMA) finally using the simulated data, the efficiency of the proposed method in the moving average diagram are evaluated. Different degrees of correlation are evaluated.

Abstract Traditional control charts are based on the basic assumption that process data are sequentially independent of each other and have a normal distribution. However, in many real-world cases, including chemical and continuous processes, this basic assumption does not exist and there is a kind of autocorrelation between the data collected from the process. The use of traditional control charts in autocorrelated processes is unreliable and increases false alarms. One of the methods developed to control autocorrelated processes is to identify the structure of the process time series and use the residual values ​​to control the process. In this paper, a model based on neuro-fuzzy adaptive systems is designed to identify the structure of the time series and use the prediction. Finally, it is AR(2). Also, residual control charts based on this system for second-order autoregressive data using simulated data, the efficiency of the proposed method in the weighted moving average chart and for different degrees of correlation are evaluated, and it is shown that the proposed method has very good efficiency for data with high correlation.

Abstract Sometimes, a quality characteristic is expressed as a specific functional relationship, which is referred to as a profile in statistical quality control. In this paper, linear profile analysis in Phase I control charts is investigated from a new perspective. In statistical quality control, it is usually assumed that the process has a constant mean. However, there may be processes where the mean of the parameters is not constant over time, and this variability is an inherent characteristic of the process. For controlling such processes, most existing control chart methods are ineffective. To statistically monitor such processes in Phase I, it is necessary to segment the process into sections where it is stable and consistent before performing statistical process control. For process segmentation, few studies have proposed the use of time-based clustering techniques, and the existing studies have mainly focused on univariate data. In this paper, we develop a process segmentation method for profile monitoring. Different segmentation algorithms for functional data are compared, and the results are reported. Studies show that the functional data clustering algorithm based on the Fuzzy c-means method performs well for profile segmentation.