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

摘要

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 (χ~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 largescale, complex process data.