Concurrent Monitoring and Isolation of Static Deviations and Dynamic Anomalies with a Sparsity Constraint

Abstract

In modern process industries, elaborate monitoring and isolation of various disturbances and faults are needed for reliable and efficient system operation. The classic process-monitoring and fault-diagnosis methods can grasp the correlation between variables, and thus, only take care of abnormal situations caused by the corruption of the correlation relationship. However, dynamics anomalies are even more noteworthy as they reflect more internal details of the system dynamic behaviour under specific situations, and more importantly, can cause severe failures and spread to a broader range of areas while evolving over time. In this paper, a monitoring-and-isolation strategy is proposed to concurrently detect and isolate faults of static deviations and dynamic anomalies. The natural sparsity of the faulty variables is used to overcome the limitations of unknown fault directions and insufficient erroneous measurements, thereby translating the isolation problem into a quadratic programming problem with a sparsity constraint and solved by the least absolute shrinkage and selection operator (LASSO). The case study shows the advantages of the proposed method in monitoring and isolating static deviations and dynamic anomalies.