An Efficient Method to Discover Association Rules of Mode-Dependent Alarms Based on the FP-Growth Algorithm

State-based alarming is an advanced technique to reduce nuisance alarms and alarm floods by suppressing alarms associated with certain conditions or operating modes. To implement this technique, the key is to find the associations between operating modes and alarms. In practice, this is mainly done based on the experience of plant operators and expert knowledge of process engineers, and thus is very time consuming. Therefore, this paper proposes a data driven method to discover the association rules of mode-dependent alarms for both single and multiple operating modes from the historical Alarm and Event (A&E) logs in an efficient way based on a data mining approach named FP-Growth. The effectiveness of the proposed method is demonstrated by an industrial case study.