Statistical process monitoring based on dissimilarity of process data

TLDR

Multivariate statistical process control is widely used to monitor chemical processes with highly correlated variables. The study proposes a novel monitoring method that detects operating condition changes by tracking the distribution of process data. The method, called DISSIM, uses a dissimilarity index to compare data set distributions and is evaluated against conventional MSPC on simulated 2×2 and Tennessee Eastman process data. DISSIM, particularly its dynamic variant, outperforms conventional MSPC when an appropriate time‑window size is chosen.

Abstract

Abstract Multivariate statistical process control (MSPC) has been widely used for monitoring chemical processes with highly correlated variables. In this work, a novel statistical process monitoring method is proposed based on the idea that a change of operating condition can be detected by monitoring a distribution of process data, which reflects the corresponding operating conditions. To quantitatively evaluate the difference between two data sets, a dissimilarity index is introduced. The monitoring performance of the proposed method, referred to as DISSIM, and that of the conventional MSPC method are compared with their applications to simulated data collected from a simple 2 × 2 process and the Tennessee Eastman process. The results clearly show that the monitoring performance of DISSIM, especially dynamic DISSIM, is considerably better than that of the conventional MSPC method when a time‐window size is appropriately selected.