when a change occurs in a process, one expects to receive a signal from a control chart as quickly as possible. upon the receipt of signal from the control chart a search for identifying the source of disturbance begins. however, searching for assignable cause around the signal time, due to the fact that the disturbance may have manifested itself into the rocess sometimes back, may not always l...

This paper develops a new multivariate control charting method for vector autocorrelated and serially correlated processes. The main idea is to propose a Bayesian multivariate local level model, which is a generalization of the Shewhart-Deming model for autocorrelated processes, in order to provide the predictive error distribution of the process and then to apply a univariate modified EWMA con...

a manufacturing process cannot be released to production until it has been proven to be stable. also, we cannot begin to talk about process capability until we have demonstrated stability in our process. this means that the process variation is the result of random causes only and all assignable or special causes have been removed. in complicated manufacturing processes, such as drilling proces...

The reliability data is getting used to monitor and improve the quality of products or services. Nowadays, most of products or services are the results of processes with dependent stages referred to as multi-stage process. In these processes, the quality characteristics are affected by the quality characteristics in the previous stages, called as cascade property. In some cases, it is not possi...

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...

Control limits in traditional Multivariate Quality Control Charts (MQCC), such as Hotelling's T control chart, Multivariate Cumulative Sum (MCUSUM) and Multivariate Exponentially-Weighted Moving Average (MEWMA) control charts are based on multivariate normal assumption. This assumption is not usually satisfied, especially in real life applications. The purpose of a control chart based on suppor...