Alarm systems play important roles for the safe and efficient operation of modern industrial plants. Critical alarms are configured with a higher priority and are safety related among many other alarms. If critical alarms can be predicted in advance
the operator will have more time to prevent them from happening. In this paper
we present a dynamic alarm prediction algorithm
which is a probabilistic model that utilizes alarm data from distributed control system
to calculate the occurrence probability of critical alarms. It accounts for the local interdependences among the alarms using the n-gram model
which occur because of the nonlinear relationships between variables. Finally
the dynamic alarm prediction algorithm is applied to an industrial case study.
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