Fault detection and root cause identification are both important tasks in Multivariate Statistical Process Control (MSPC) for improving process and product quality. Most traditional control charts, including Hotelling’s T 2 chart and the Multivariate Exponential Weighted Moving Average (MEWMA) chart, separate the two tasks into independent and successive procedures by signaling the existence of...

 The repeatability and reproducibility (R&R) study—also called a gauge capability study—has been employed as part of the statistical process control program in many organizations. The objective of the study is to determine whether a measurement procedure or instrument is adequate for monitoring the performance of a process. The classical control chart method can be easily performed and ...

Control charts are widely implemented in firms to establish and maintain statistical control of a process which leads to the improved quality and productivity. Design of control charts requires that the engineer selects a sample size, a sampling frequency and the control limits for the chart. In this paper, a possible combination of design parameters is considered as a decision making unit whic...

Statistical control charts are useful tools in monitoring the state of a manufacturing process. Control charts are used to plot process data and compare it to the limits set for the process. Points plotting outside these limits indicate an out-of-control condition. Standard control charting procedures, however, are limited in that they cannot take into account the case when data is of a fuzzy n...

Measurement process must be regularly monitored and evaluated to make accurate decisions about manufacturing process condition or products quality. It is able by statistical methods and tools usage. A paper is a discussion about necessity for measurement systems analysis (MSA) during manufacturing process. There has drawn up a new attitude to MSA which can be characterized by taking the measure...

Monitoring Markov Dependent Observations with a Log-Likelihood Based CUSUM Shabnam Mousavi and Marion R. Reynolds, Jr. Department of Statistics, The Pennsylvania State University, University Park, PA 16802-2111, U.S.A., Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany, Departments of Statistics and Forestry, Virginia Polytechnic Institute and State University, B...