Abstract

Alarm systems are essential for the safe and efficient operation of process industries. However, complex plant connectivity and process interactions could cause many correlated alarms in practice and thus compromise alarm system performance. To address correlated alarms, it is desired that alarm correlations are discovered from historical Alarm and Event (A&E) logs, so the obtained results could help improve alarm configurations or design suppression strategies. Motivated by this problem, a systematic method to extract alarm correlation is proposed in this work and the contributions are: (1) Correlated alarms and their occurrence orders are captured as correlation patterns through pattern mining, and such patterns are characterized by statistical features. (2) Alarm correlations and their statistical features are visualized as network graphs to indicate process interactions and identify alarms for prioritized analysis. To demonstrate the effectiveness of the proposed method, case studies are provided using an industrial simulation benchmark Vinyl Acetate Monomer (VAM) plant model.