Root Cause Identification of Industrial Alarm Floods Using Word Embedding and Few-Shot Learning

Abstract

Alarm systems are commonly deployed in modern industrial facilities to monitor process operations. However, due to the presence of nuisance alarms and alarm floods, their efficiencies are much degraded. Especially, alarm floods are among the most difficult issues in industrial alarm management and recognised as the main causes of many industrial accidents. To address alarm floods, this paper proposes a Root Cause Identification method (RCI) for industrial alarm floods based on word embedding and few-shot learning. The contributions are threefold: 1) A textual encoding method based on word embedding is proposed to convert alarm messages into numerical word vectors that can be used in the modeling of RCI; 2) an alarm priority based adaptive weighting strategy is designed to make the RCI more sensitive to alarms of higher priorities and appearing earlier; 3) a few-shot learning method based on the long short-term memory is adapted to identify root causes of alarm floods based on limited instances of labeled data. The effectiveness and superiority of the proposed method are demonstrated by a case study based on data from the Vinyl Acetate Monomer (VAM) public model.