Overview of PCA-Based Statistical Process-Monitoring Methods for Time-Dependent, High-Dimensional Data

Abstract

High-dimensional and time-dependent data pose significant challenges to statistical process monitoring. Dynamic principal-component analysis, recursive principal-component analysis, and moving-window principal-component analysis have been proposed to cope with high-dimensional and time-dependent features. We present a comprehensive review of this literature for the practitioner encountering this topic for the first time. We detail the implementation of the aforementioned methods and direct the reader toward extensions that may be useful to their specific problem. A real-data example is presented to help the reader draw connections between the methods and the behavior they display. Furthermore, we highlight several challenges that remain for research in this area.

• Autocorrelation
• Nonstationarity
• Principal-Component Analysis

Additional information

Notes on contributors

Bart De Ketelaere

Dr. De Ketelaere is Research Manager in the Division of Mechatronics, Biostatistics and Sensors (MeBioS). His email is [email protected].

Mia Hubert

Dr. Hubert is Professor in the Department of Mathematics. Her email is [email protected].

Eric Schmitt

Mr. Schmitt is Doctoral Student in the Department of Mathematics. His email is [email protected]. He is the corresponding author.