Reconstruction-based contribution for process monitoring
Automatica (Journal of IFAC)
| Hi-index | 0.00 |
Investigating the root causes of abnormal events is a crucial task for an industrial chemical process. When process faults are detected, isolating the faulty variables provides additional information for investigating the root causes of the faults. Numerous data-driven approaches require the datasets of known faults, which may not exist for some industrial processes, to isolate the faulty variables. The contribution plot is a popular tool to isolate faulty variables without a priori knowledge. However, it is well known that this approach suffers from the smearing effect, which may mislead the faulty variables of the detected faults. In the presented work, a contribution plot without the smearing effect to non-faulty variables was derived. An industrial example, correctly isolating faulty variables and diagnosing the root causes of the faults for the compression process, was provided to demonstrate the effectiveness of the proposed approach for industrial processes.