An Automated Data-Driven Method to Detect Mode-Based Alarms

A variety of alarm management techniques are available to improve the performance of alarm systems and avoid alarm overloading; in particular, the state-based alarming strategy has been widely used in practice to remove noninformative alarms that are caused by the switching of operating modes. However, the configuration of mode-based alarming strategies relies on proficient process knowledge, and thus is time and resource intensive. To address this problem, this paper presents a completely automated data-driven technique to detect mode-based alarms from historical Alarm & Event (A&E) logs. The major contributions are: 1) the detection of mode-based alarms is formulated as a hypothesis testing problem; 2) systematic detection methods are proposed to process A&E data and output final results as association rules. The efficacy of the proposed method is illustrated by industrial case studies involving real A&E data.