The presence of asymmetric data in production processes or service operations has prompted the development new monitoring schemes. In this article, an adapted version exponentially weighted moving averages (EWMA) control chart with dynamic limits is proposed to monitor mean samples from skew-normal distribution. detection ability online was investigated by simulating average run length (ARL) pe...

usually, in monitoring schemes the nominal value of the process parameter is assumed known. however, this assumption is violated owing to costly sampling and lack of data particularly in healthcare systems. on the other hand, applying a fixed control limit for the risk-adjusted bernoulli chart causes to a variable in-control average run length performance for patient populations with dissimilar...

in some statistical process control applications, the quality of the product is characterized by thecombination of both correlated variable and attributes quality characteristics. in this paper, we propose anovel control scheme based on the combination of two multi-layer perceptron neural networks forsimultaneous monitoring of mean vector as well as the covariance matrix in multivariate-attribu...

Due to more frequent use of short production runs and the need for shorter setup times in order to make these runs cost-effective, there has lately been a great deal of interest in the statistical properties of X ñ control charts with control limits that are based on unknown process parameters. Typically the true process distribution can be assumed to be normal, but the process mean μ and varia...

Abstract: Two approaches for constructing control charts to monitor multivariate attribute processes when data set is presented in linguistic form are suggested. Two monitoring statistics 2 f T and are developed based on fuzzy and probability theories. The first is similar to the Hotelling’s statistic and is based on representative values of fuzzy sets. The distribution of statistic, being a li...

Monitoring multi-attribute processes is an important issue in many quality control environments. Almost all the priory proposed control charts utilize equal weights for each Attribute Quality Characteristics (AQCs). In such condition, there is no priority among AQCs. But in real-world, compensatory may exist. Hence due to some applied reasons such as function or efficiency, unequal weights for ...

This paper describes the application of multiresolution analysis to the states and the model residuals calculated through the application of canonical variate analysis (CVA) for process performance monitoring. Applying Hotelling T to the states and residuals and calculating the statistical limits will materialise in an excess of false alarms since the CVA model states and model residuals are ob...

I N STATISTICAL process control (SPC), one major concern is whether there has been a change of the distribution from the target in the process. To answer this question, we have to distinguish the variation due to real change of distribution (assignable causes) from that due to random error (chance causes). Many kinds of control charts serve this purpose. When there is no change and the process ...

Control charts are one of the most important tools in statistical process control that lead to improve quality processes and ensure required quality levels. In traditional control charts, all data should be exactly known, whereas there are many quality characteristics that cannot be expressed in numerical scale, such as characteristics for appearance, softness, and color. Fuzzy sets theory is p...