An optimally designed distribution-free CUSUM procedure for tri-aspect surveillance of continuous processes

In the past few decades, research on nonparametric process monitoring schemes mainly dealt with the uni-aspect or bi-aspect schemes, focusing on monitoring process location or scale separately or jointly. Another critical process characteristic, namely, the process shape, is not explicitly dealt with in-depth in most existing charting schemes. In classical hypothesis testing, some recent literature clearly showed that the multi-aspect test statistics, although designed for very restrictive alternatives, often perform as well or better than many statistics for arbitrary distributional shifts. They are often better than Kolmogorov-Smirnov or Cramer-von Mises statistics and some empirical likelihood-based test statistics. The current paper aims to use a tri-aspect statistic to design a distribution-free Phase-II Cumulative Sum (CUSUM) charting scheme for monitoring any arbitrary process changes in the process. The proposal is nonparametric and is equivalent to an unknown standard case. It reflects, in addition, which parameters, among location, scale, or shape, are more responsible for a signal. The construction of the CUSUM scheme from an existing tri-aspect Shewhart-type chart is simple, so significant attention is devoted to determining a near-optimal reference parameter of the chart in keeping an unknown shift type in mind. Comparisons of the optimal performance of various competitors are considered in terms of the median run length (MRL) metric. The proposed charting scheme designed with the tri-aspect statistic compares highly favorably with many existing SPM schemes. The same is evident from our findings based on the Monte-Carlo simulation. Finally, the proposed schemes are illustrated with a flow-width measurement monitoring example.