Hierarchical Frequent Sequence Mining Algorithm for the Analysis of Alarm Cascades in Chemical Processes

Abstract

Faults and malfunctions on complex chemical production systems generate alarm cascades that hinder the work of the operators and make fault diagnosis a complex and challenging task. The core concept of our work is the incorporation of the hierarchical structure of the technology in a multi-temporal sequence mining algorithm to group the large number of variables. The spreading of the effect of malfunctions over the plant is thoroughly traceable on the higher levels of the hierarchy, while the critical elements of the spillover effect can be detected on the lower levels. Confidence-based goal-oriented measures have been proposed to describe the orientation of fault propagation providing a good insight into the causality on a local level of the process, while the network-based representation yields a global view of causal connections. The effectiveness of the proposed methodology is presented in terms of the analysis of the alarm and event-log database of an industrial delayed-coker plant, where the complexity of the problem and the size of the event-log database requires a hierarchical constraint-based representation.