Journal of Quality Engineering and Management

Abstract  
 If the identification of small changes in the production process is intended, the Moving Averaging Control Chart (EWMA) is a good alternative to the X control chart. In situations where a large sample of the population cannot be extracted due to economic constraints, the Simple Random Sampling Scheme (SRS) may not be accurate enough, in which case the RSS can be used. Appeared. In this paper, for the first time, the economic and statistical-economic design of the EWMA control chart under the RSS design is reviewed. By presenting numerical results, the advantages of statistical-economic design over economic design are shown. The results show that costs in statistical-economic design have increased slightly compared to economic design, but due to the low false alarm rate are in line with statistical quality control objectives and at the same time reduce costs. , Controls the quality of the product at the desired level of error and high power. Keywords: Statistical-economic design, ranking set sampling, shock model. 

Abstract Nowadays due to diversity of customer demand and short time for product evolution cycle in market, manufacturing strategy is tended to short run processes characterized by high diversity and low volume. Hence, statistical process control for such processes because of inspection restrictions in a short period is a special and significant practice. In such circumstances, control charts in Phase I cannot be performed and also correct estimations are not available for appraising process parameters. Therefore, it is essential to design new control charts and to utilize them instead of traditional control charts for monitoring such processes. On the other hand, sometimes quality characteristics are described by a relationship between a response variable and one or more explanatory variables, referred to as profile in the literature. In this paper for monitoring quality characteristics delineated by simple linear profiles in short run processes, three control charts are designed to monitor profile parameters (intercept, slope and standard deviation).These control charts have a capability to update the parameter estimations along with new observations and concurrent checks of the out-of-control conditions. The Performance of the proposed method has been compared with competitor control chart by using simulation studies and average run length criterion. The results show that proposed method in some parameters has better performance compared to the competitor control chart in detecting moderate and large shifts.              

Abstract The change point is a useful concept in the control of statistical process that assists quality engineers in finding assignable causes and improving the quality of a product or process. It also reduces the time and cost of detecting assignable causes. In this paper, an artificial neural network approach is used to estimate the step change point in phase II monitoring of the mean of multiple linear profiles. the performance of the probabilistic neural network is evaluated in order to estimate the change point utilizing Monte Carlo Simulation. The results of simulations show the fact that the network recommended in estimating the change point has entirely better performance than maximum likelihood estimator in small shifts, considering mean square error criteria, but maximum likelihood estimator method owns better performance in medium to large shifts. In general, in all shift types, maximum likelihood estimator has a better performance in terms of precision, and the proposed probabilistic neural network performs better in terms of accuracy. In addition, another advantage of the proposed approach is the fact that contrary to the maximum likelihood estimator approach, it does not require any knowledge about the change type and can appropriately estimate any kind of change point as well. 
 

Abstract Many engineers and researchers base their reliability models on the hypothesis that the components of a system work statistically independently and fail. This assumption is often violated in practice because, specific environmental and systemic factors affect the performance of components and thus contribute to correlated failures, which can reduce the reliability of a system. Determining the component correlation index and its effect on system reliability is necessary to be able to require models to explicitly combine and coordinate continuous failures and provide an accurate estimate of system reliability. Previous approaches to correlation modeling are limited to systems that consist of two or three components or assume that the operation of the components is statistically independent. In this study, while considering the dependence between component functions, a model is presented to consider this dependence and calculate the reliability of series, parallel, k-out of-n, parallel-series and series-parallel systems. To better understand the problem, examples are provided with sensitivity analysis in which the components are functionally interdependent. These examples show how the correlation between the components has affected their performance.

Abstract The purpose of this paper is to investigate the reliability parameter R = P (X <Y) based on samples with type 2 incremental censorship in which X and Y are independent random variables with generalized inverse exponential distribution with different shape parameters and the same scale parameter. The maximum likelihood estimator (MLE) and the nonlinear estimator with uniform uniform variance (UMVUE) of the parameter R are obtained and different confidence intervals are provided. Also, Bayesian R estimator and HPD confidence interval using Gibbs sampling method are proposed. Monte Carlo simulations have been performed to compare the performance of different methods.

Abstract In this research, using Markov model, general formula for calculating reliability and MTTF, which are the main engineering factors in quality in systems with redundancy of 1 of n, from two methods of solving differential equations and shortcut method (direct calculation of MTTF without the need for function Reliability is obtained by using transition matrices in the Markov model, which are sometimes briefly mentioned in the article. One of the remarkable results of this research is that the possibility of estimating parameters for any desired number of n has been facilitated. Also, using the obtained functions, the effect of increasing add-on items on improving reliability in these systems (with ready-to-serve and active modes and repairable and non-repairable components), along with factors such as repair rate and reliability of systems. Switching has been evaluated. Mathematica software has been used to perform calculations and data analysis.