Independent component analysis application for fault detection in process industries: Literature review and an application case study for fault detection in multiphase flow systems

In process industries, early detection and diagnosis of faults is crucial for timely identification of process upsets, equipment and/or sensor malfunctions. Machine learning techniques using process data can be used as efficient process monitoring tools and is an active research area in the past two decades. The technique of independent component analysis (ICA) is a viable alternative to the widely used principal component analysis method. In this article, the basic ICA technique, its advantages, limitations and the various improvements proposed over the years are reviewed. Further, a detailed survey of ICA based techniques for process monitoring is presented. Finally, the application of ICA along with selection of independent components by negentropy calculation and control limit and monitoring index calculation is illustrated by an industrial case study of multiphase flow system.

Automated summary

In process industries, early detection and diagnosis of faults using machine learning techniques is crucial for timely identification of process upsets, equipment and/or sensor malfunctions. This article reviews the basic technique of independent component analysis (ICA) as a viable alternative to principal component analysis, and presents a detailed survey of ICA-based techniques for process monitoring. The application of ICA in an industrial case study of multiphase flow system is illustrated, along with the selection of independent components by negentropy calculation and control limit and monitoring index calculation.