A maximum-entropy-based method for alarm flood prediction

Abstract Alarm floods are the phenomena of presenting too many alarms in a short time period to exceed abilities of industrial plant operators in making proper responses. This paper proposes a maximum-entropy-based method to predict upcoming alarms for an occurring alarm flood. The proposed method has two important features: all currently-occurred alarms are exploited for prediction, and upcoming alarms are given with quantitative probabilities. By contrast, existing alarm prediction methods either use most-recent (not all) occurred alarms in prediction, or cannot predict specific alarms with quantitative probabilistic values. The proposed method takes all historical alarm flood sequences into account to establish relationships between currently-occurred alarms and upcoming alarms, and formulates an optimization problem to maximize conditional entropies of upcoming alarms. The effectiveness of the proposed method is validated by numerical examples, one of which is on the well-accepted benchmark of Tennessee Eastman process.