Parameter optimization for a modified MEWMA control chart based on a PSO algorithm

摘要

The standard multivariate exponentially weighted moving average (MEWMA) control chart with a constant smoothing parameter or diagonal matrix is based on the assumption that the samples obey standard normal distribution. With improvements in manufacturing quality and product complexity, there is always correlativity among quality characteristics, and samples will not always obey standard normal distribution. Considering the correlativity among quality characteristics, a new modified general MEWMA (GEWMA) control chart is proposed, and its performance is analyzed. Based on the Particle Swarm Optimization (PSO) algorithm, a smoothing matrix optimized under certain conditions is selected and applied to a sample analysis. As a result of the parameter combination chosen by PSO, the statistic function of the GEWMA control chart is better than that of the full matrix MEWMA (FEWMA) control chart.