Structure Feature Extraction for Hierarchical Alarm Flood Classification and Alarm Prediction

Alarm flood classification and alarm prediction are significant ways to assist the on-site operators to manage alarm floods and maintain process safety. The two tasks are interdependent considering the decisive role of different alarm floods on the arising alarms. To give comprehensive consideration of both, this work proposed a hierarchical strategy for alarm flood classification and alarm prediction leveraging the structure feature of alarm floods. The structure feature aims at revealing the sparse causal dependencies among alarm variables. It is achieved by a deep learning model under the guidance of a designed objective function that probabilistically parametrizes causal dependencies with sparsity constraint. Due to its interpretable physical meaning, desirable robustness and discriminate properties are achieved, allowing a win-win situation for both tasks. Based on the structure features, the hierarchical strategy is given, where an overall classifier is built while prediction models are trained for each category. The classifier trained by structure features is predisposed to generate satisfactory early classification results, enabling timely prediction. For the prediction, the structure features are also used to incorporate temporal features to achieve better performance. Experimental results illustrate the interpretability of structure features and show the feasibility of the proposed hierarchical strategy. Note to Practitioners-During alarm floods, the alarm patterns are different than usual and the generic prediction model may fail. The focus of this study is to achieve a win-win situation for both alarm flood classification and prediction, thereby providing comprehensive information required for handling alarm floods. Considering the arising alarm is strongly affected by the type of current alarm flood, a hierarchical alarm flood classification and alarm prediction strategy is given. It exploits the essential characteristic of alarm interactions in alarm floods to generate robust and early classification results, allowing timely predictions by category. In this way, the performance of both tasks can be guaranteed. The proposed method requires labeled historical alarm flood data.