The most popular tool used in the industry for monitoring a process is the Shewhart control chart. The major disadvantage of the Shewhart control chart is that it is not very efficient in detecting small process average shifts. To increase the sensitivity of Shewhart control charts to small shifts additional supplementary runs rules has been suggested. In this paper we introduce and study the m...

An extremely important issue in quality management is monitoring and diagnosing processes, and, subsequently, supervising them using so-called control charts. In typical production charts with constant parameters are commonly used, such as x-R, x-s, CUSUM, EWMA others, which, most cases, effective tools for process stability evaluation. Charts considered untypical (in statistical control) those...

This paper proposes three synthetic-type control charts to monitor the mean time-between-events of a homogenous Poisson process. The first proposed chart combines an Erlang (cumulative time between events, Tr ) chart and a conforming run length (CRL) chart, denoted as Synth-Tr chart. The second proposed chart combines an exponential (or T) chart and a group conforming run length (GCRL) chart, d...

Traditional literature on statistical quality control discusses separately multivariate control charts for independent processes and univariate control charts for autocorrelated processes. We extend univariate residual monitoring to the multivariate environment, and propose using vector autoregressive residuals (VAR) to monitor multivariate processes in the presence of serial correlation. We ma...

Due to competition in the market, organization must have quality improvement program. Statistical quality control and especially control charts are proven quality improvement techniques. Control charts are based on the quality characteristics measurement in the course of time. There are some situations such as measurement error, sophisticated measurement instruments, costly skilled inspectors, ...

statistical process control (spc) charts play a major role in quality control systems, and their correct interpretation leads to discovering probable irregularities and errors of the production system. in this regard, various artificial neural networks have been developed to identify mainly singular patterns of spc charts, while having drawbacks in handling multiple concurrent patterns. in this...

CCC-r control chart is a monitoring technique for high yield processes. It is based on the analysis of the number of inspected items until observing a specific number of defective items.  One of the assumptions in implementing CCC-r chart that has a significant effect on the design of the control chart is that the inspection is perfect. However, in reality, due to the multiple reasons, the...

Statistical process control is a very useful method to improve the product quality and reduce reworks and scraps. In a production environment, control charts are the most important tool to determine whether a process is in-control or out-of-control. Control charts are to detect the occurrence of the shifts in a process rapidly so that their causes can be found and the necessary corrective actio...

Solubility data of refrigerant/lubricant mixtures have been modeled with a cubic equation of state. The systems studied are binary mixtures of hydrofluorocarbons (R-32, R-125, R-134a and R-143a) and alkylbenzene oils (viscosity grades 15 and 32). These partially miscible mixtures are well correlated with the present model, which can be used for calculating the pressure-temperature-composition r...

In this paper, a remedial measure is first proposed to eliminate the effect of autocorrelation in phase-ІІ monitoring of autocorrelated polynomial profiles, where there is a first order autoregressive (AR(1)) relation between the error terms in each profile. Then, a control chart based on the generalized linear test (GLT) is proposed to monitor the coefficients of polynomial profiles and an R-c...