Abstract
Abstract 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. A continuous stirred tank reactor (CSTR) example was provided to demonstrate that the proposed approach is not only capable of isolating faulty variables of a simple fault but also a complex fault.
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