Monitoring independent components for fault detection

TLDR

Chemical processes are driven by fewer essential variables than measured, and ICA is an emerging technique to extract independent variables as linear combinations of measured variables. The study proposes a new ICA‑based statistical process control method to extract and monitor essential variables, aiming to improve process‑monitoring performance. The method applies ICA to derive independent components, then evaluates fault‑detection performance against conventional cMSPC using PCA on a simple four‑variable system and a continuous‑stirred‑tank‑reactor process. Simulations demonstrate that ICA‑based SPC outperforms conventional cMSPC in fault detection.

Abstract

Abstract A chemical process has a large number of measured variables, but it is usually driven by fewer essential variables, which may or may not be measured. Extracting such essential variables and monitoring them will improve the process‐monitoring performance. Independent component analysis (ICA) is an emerging technique for finding several independent variables as linear combinations of measured variables. In this work, a new statistical process control method based on ICA is proposed. For investigating the feasibility of its method, its fault‐detection performance is evaluated and compared with that of the conventional multivariate statistical process control (cMSPC) method using principal‐component analysis by applying those methods to monitoring problems of a simple four‐variable system and a continuous‐stirred‐tank‐reactor process. The simulated results show the superiority of ICA‐based SPC over cMSPC.