Usually, there are two phases in constructing a multivariate control chart. Phase I is to estimate the in-control process parameters and to establish control limits using historical data. Phase II is when the control limits are used to monitor the process. This paper focuses on determining the optimal number of samples in Phase I since number of samples affect the cost of quality control in pra...

In a wind tunnel process, Mach number is the most important parameter. However, it difficult to measure directly, especially in multimode operation leading difficulty process monitoring. Thus, necessary indirectly by utilizing data-driven methods, and based on which, monitor status of process. this paper, therefore, flow field system monitoring strategy proposed. Since strongly nonlinear system...

Some quality control schemes have been developed when several related quality characteristics are to be monitored. The familiar multivariate process monitoring and control procedure is the Hotelling’s T 2 control chart for monitoring the mean vector of the process. It is a direct analog of the univariate shewhart x̄ chart. As in the case of univariate, the ARL improvements are very important par...

We develop a non-parametric method to monitor several process variables simultaneously. This method is based on the concept of data depth, which indicates the position of an observation with respect to the data cloud. Principal components are used to reduce the dimensionality of the process, and a non-parametric chart is used to monitor some of the principal components instead of the original v...

the most well-known uni-arribute control chart used to monitor the number of nonconformities per unit is the shewhart type c-chart. in this paper, a new method is proposed in an attempt to reduce the false alarm rate in the c-chart. to do this, the decision on beliefs (dob) concept is first uti [1] corresponding author e-mail: [email protected]   lized to design an iterat...

In recent years, some authors have incorporated the penalized likelihood estimation into designing multivariate control charts under the premise that in practice typically only a small set of variables actually contributes to changes in the process. The advantage of the penalized likelihood estimation is that it produces sparse and more focused estimates of the unknown population parameters whi...

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...

Control charts for monitoring of process variance are developed based on Shewhart, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts for mean. In all these variance control charts, log transformation of the sample variance is used. The design procedure of this chart is complex and it is poorly understood by the industry. In this paper a EWMA chart for monito...

The basic control chart consists of sampling from a process over time and charting process measurements. For as long as the measurements fall inside control limits (L), the process is considered in-control. In feed production, it is customary to set the control limits at two standard deviations. This practice does not consider the probability of both Type I and Type II errors in addition to all...

Consumers make complaint about the state of home-made goods. Some consumers claim that foreign goods are high quality compared to We discovered many indigenous industries no more in existence and so this brought desire carry out research work as find whether products from our brewery industry fall within acceptance region devoid consumers’ complaint. For clarity easy handling topic, data were c...