Journal of Quality Engineering and Management

Abstract The change point is a useful concept in the control of statistical process that assists quality engineers in finding assignable causes and improving the quality of a product or process. It also reduces the time and cost of detecting assignable causes. In this paper, an artificial neural network approach is used to estimate the step change point in phase II monitoring of the mean of multiple linear profiles. the performance of the probabilistic neural network is evaluated in order to estimate the change point utilizing Monte Carlo Simulation. The results of simulations show the fact that the network recommended in estimating the change point has entirely better performance than maximum likelihood estimator in small shifts, considering mean square error criteria, but maximum likelihood estimator method owns better performance in medium to large shifts. In general, in all shift types, maximum likelihood estimator has a better performance in terms of precision, and the proposed probabilistic neural network performs better in terms of accuracy. In addition, another advantage of the proposed approach is the fact that contrary to the maximum likelihood estimator approach, it does not require any knowledge about the change type and can appropriately estimate any kind of change point as well. 
 

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.