quality control plays an important role in increasing the product quality. fuzzy control charts are more sensitive than shewhart control chart. hence, the correct use of fuzzy control chart leads to producing better-quality products. this area is complex because it involves a large scope of industries, and information is not well organized. in this research, we provide a literature review of th...

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...

Control charts have been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior and quality improvement. In particular, multivariate control charts have been effectively used when a process involves a number of correlated process variables. Most existing multivariate control charts were developed using the assumption of normally distributed pro...

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...

Some of the most widely-investigated control charting techniques for autocorrelated data are based on time series residuals. If the mean shift in the autocorrelated process is a sudden step shift, the resulting mean shift in the residuals is time varying and has been referred to as the fault signature. Traditional residual based charts, such as a Shewhart, CUSUM, or EWMA on the residuals, do no...

Shewhart charts are the most commonly utilised control for process monitoring in industries with assumption that underlying distribution of quality characteristic is normal. However, this may not always hold true practice. In paper, weighted-variance mean developed and their population standard deviation estimated using three subgroup scale estimators, namely deviation, median absolute trimmed ...

In manufacturing industries, development of measurement leads to increase the number of monitoring variables and eventually the importance of multivariate control comes to the fore. Statistical process control (SPC) is one of the most widely used as multivariate control chart. Nevertheless, SPC is restricted to apply in processes because its assumption of data as following specific distribution...

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 usual practice of using a control chart to monitor a process is to take samples from the process with fixed sampling interval (FSI). In this paper, a synthetic X control chart with the variable sampling interval (VSI) feature is proposed for monitoring changes in the process mean. The VSI synthetic X chart integrates the VSI X chart and the VSI conforming run length (CRL) chart. The propose...

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 ...