Abstract: Multivariate quality control charts show some advantages to monitor several variables in comparison with the simultaneous use of univariate charts, nevertheless, there are some disadvantages. The main problem is how to interpret the out-ofcontrol signal of a multivariate chart. For example, in the case of control charts designed to monitor the mean vector, the chart signals showing that it must be accepted that there is a shift in the vector, but no indication is given about the variables that have produced this shift. The MEWMA quality control chart is a very powerful scheme to detect small shifts in the mean vector. There are no previous specific works about the interpretation of the out-of-control signal of this chart. In this paper neural networks are designed to interpret the out-of-control signal of the MEWMA chart, and the percentage of correct classifications is studied for different cases.
Abstract: In some real applications of Statistical Process Control
it is necessary to design a control chart to not detect small process
shifts, but keeping a good performance to detect moderate and large
shifts in the quality. In this work we develop a new quality control
chart, the synthetic T2 control chart, that can be designed to cope with
this objective. A multi-objective optimization is carried out employing
Genetic Algorithms, finding the Pareto-optimal front of
non-dominated solutions for this optimization problem.