Phase I Analysis of High-Dimensional Processes in the Presence of Outliers

TL;DR

This paper introduces a robust control chart method for high-dimensional process monitoring in Phase I, effectively detecting shifts even with limited samples and outliers, validated through simulations and real-world examples.

Contribution

It proposes a new robust statistical procedure and estimator for high-dimensional process monitoring, addressing outliers and limited sample issues in Phase I analysis.

Findings

Effective detection of process shifts in high-dimensional data

Robust parameter estimation in the presence of outliers

Validated through simulations and real-world case studies

Abstract

One of the significant challenges in monitoring the quality of products today is the high dimensionality of quality characteristics. In this paper, we address Phase I analysis of high-dimensional processes with individual observations when the available number of samples collected over time is limited. Using a new charting statistic, we propose a robust procedure for parameter estimation in Phase I. This robust procedure is efficient in parameter estimation in the presence of outliers or contamination in the data. A consistent estimator is proposed for parameter estimation and a finite sample correction coefficient is derived and evaluated through simulation. We assess the statistical performance of the proposed method in Phase I in terms of the probability of signal criterion. This assessment is carried out in the absence and presence of outliers. We show that, in both phases, the proposed control chart scheme effectively detects various kinds of shifts in the process mean. Besides, we present two real-world examples to illustrate the applicability of our proposed method.