FAST AND ROBUST BIVARIATE CONTROL CHARTS FOR INDIVIDUAL OBSERVATIONS

Abstract

There are various circumstances where it is important to simultaneously monitor or control two or more related quality characteristics. Independently tracking these quality characteristics might be quite deceptive. Hotelling's T2 chart, in which the T2 statistics are generated using the classical estimates of location and scatter, is the most well-known multivariate process monitoring and control approach. It is well known that the existence of outliers in a dataset has a significant impact on classical estimators. Any statistic that is computed using the classical estimates will be distorted by even a single outlier. The non-robustness issue is investigated in this study, which also suggests four robust bivariate control charts based on the robust Gnandesikan-Kettenring estimator. This study employs four highly robust scale estimators, with the best breakdown point, namely the Qn estimator, Sn estimator, MAD estimator, and τ estimator, in order to robustify the Gnandesikan- Kettenring estimator. Through the use of a Monte Carlo simulation and a real-life data, the performance of the suggested control charts is assessed. The four techniques all outperform the traditional method and provide greater computing efficienc