A Hybrid SPC Method with the Chi‐Square Distance Monitoring Procedure for Large‐scale, Complex Process Data

Abstract

Abstract Standard multivariate statistical process control (SPC) techniques, such as Hotelling's T 2 , cannot easily handle large‐scale, complex process data and often fail to detect out‐of‐control anomalies for such data. We develop a computationally efficient and scalable Chi‐Square ( $\chi^2$ ) Distance Monitoring (CSDM) procedure for monitoring large‐scale, complex process data to detect out‐of‐control anomalies, and test the performance of the CSDM procedure using various kinds of process data involving uncorrelated, correlated, auto‐correlated, normally distributed, and non‐normally distributed data variables. Based on advantages and disadvantages of the CSDM procedure in comparison with Hotelling's T 2 for various kinds of process data, we design a hybrid SPC method with the CSDM procedure for monitoring large‐scale, complex process data. Copyright © 2005 John Wiley & Sons, Ltd.