Fault diagnosis using contribution plots with missing data approach

Investigating the root causes of abnormal events is a crucial task for an industrial 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. The benchmark example, the Tennessee Eastman process, was provided to demonstrate the effectiveness of the proposed approach.