An association rule mining approach to predict alarm events in industrial alarm floods

Abstract

Industrial operators often experience overwhelming situations during ongoing alarm floods due to high alarm rates. In such situations, real-time assistance in the form of prediction of upcoming alarm events can ease off the decision-making for industrial operators. Accordingly, this work studies alarm prediction in alarm flood situations, and the main contribution lies in a novel association rule mining approach for real-time prediction of alarm events and their corresponding times of annunciation during an ongoing alarm flood. The proposed method is capable of performing predictions at the triggering instant and modifying the predictions with the increasing of the ongoing alarm flood. The proposed method is implemented mainly in the following steps: (1) A Compact Prediction Tree (CPT) model is modified with new features, namely, the time table and co-occurrence matrix, and constructed based on historical alarm sequences; (2) an alarm relevancy detection strategy is designed to detect and eliminate irrelevant alarms in alarm floods; (3) an online alarm prediction algorithm is designed to predict upcoming alarms at the early stage of the ongoing alarm flood; (4) the confidence intervals of the time differences between the annunciations of subsequent predicted alarm events are calculated for time prediction. To demonstrate the effectiveness of the proposed method, an industrial case study based on real alarm & event logs from an oil refinery is provided.