Sequence Alignment and Dual-Graph Fusion-Based Manifold Regularization for Root-Cause Identification of Industrial Alarm Floods

Accurately identifying the root causes of alarm floods is essential for safe and efficient operations of industrial processes. While similarity analysis methods have been developed for analyzing and clustering alarm floods, they are insufficient to pinpoint root causes. Thus, recent studies have attempted to train classification models to identify alarm flood root causes usually based on substantial amounts of labeled training data, which is impractical and costly in real-world scenarios. Accordingly, this article proposes a semisupervised method named alarm dual-graph fusion-based manifold regularization for root-cause identification (RCI) of industrial alarm floods, which can achieve improved performance by utilizing both limited labeled alarm flood sequences and substantial unlabeled sequences. The contributions are as follows: first, a local sequence alignment-based graph construction method is proposed to construct an undirected graph for historical alarm flood sequences; second, a modified manifold regularization method based on dual-graph Laplacian fusion is developed for semisupervised alarm flood RCI modeling; third, an online RCI strategy is devised for recognition of root causes of incoming alarm flood sequences. The effectiveness of the proposed method is demonstrated by a case study with alarm data generated by the public vinyl acetate monomer process simulation model.