Journal of Quality Engineering and Management

Abstract In many statistical process control applications, the relationship between a response variable and one or more control variables is evaluated by a function called a profile. Profiles are divided into different types according to the nature of the response variable, such as linear and nonlinear profiles. In this research, a new control diagram based on the generalized linear test approach and fractional regression is presented to monitor multiple linear profiles with interactions in phase 2. The simulation results of the proposed control diagram show its much better performance than the control diagram based on the least squares error method.

Abstract In the statistical control of multivariate processes, two or more quality characteristics must be controlled simultaneously. In controlling such processes, two main goals must be achieved. The first goal is to detect out-of-control conditions and the second goal is to identify the quality characteristics that cause the deviation when an out-of-control condition occurs. In this research, ways to achieve the first goal are investigated and methods for monitoring the multivariate variance-covariance matrix in phase 2 are presented. The main goal of phase 2 is to quickly detect shifts. In this paper, two methods for monitoring the multivariate variance-covariance matrix in phase 2 are presented and the shift in one of the quality characteristics of case 1 (average trail length) and the detection of ARL are investigated. Simulation results show that the proposed methods reduce the out-of-control condition more quickly.