Comparison of Novelty Score-Based Multivariate Control Charts

Abstract

Control charts are widely used in various industries to improve product quality. One recent trend in developing control charts is based on novelty score algorithms that can effectively describe reality and reflect the unique characteristics of the data being monitored. In this study, we compared eight novelty score algorithms—the T², Local T², D_max, D_mean, K², the k-nearest neighbor data description, the local density outlier factor, and the hybrid novelty score (HNS)—in terms of their average run length performance. A rigorous simulation was conducted to compare the novelty score-based multivariate control charts under both normal and non-normal scenarios. The simulation showed that in both normal and lognormal scenarios, D_max-based control charts produced the most promising results. In skewed distribution with high kurtosis non-normal scenarios, HNS- and K²-based control charts performed best. In symmetric with kurtosis non-normal scenarios, local T²-based control charts outperformed the others.

Keywords:

• Data mining
• Novelty score
• Multivariate control charts
• Quality control

Additional information

Funding

This research was supported by Brain Korea 21 PLUS and Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning (2013007724).