Due to the abrupt
nature of the chemical process, a large number
of alarms are often generated at the same time. As a result of the
flood of alarms, it largely hinders the operator from making accurate
judgments and correct actions for the root cause of the alarm. The
existing diagnosis methods for the root cause of alarms are relatively
single, and their ability to accurately find out complex accident
chains and assist decision making is weak. This paper introduces a
method that integrates the knowledge-driven method and the data-driven
method to establish an alarm causal network model and then traces
the source to realize the alarm root cause diagnosis, and develops
the related system modules. The knowledge-driven method uses the hidden
causality in the optimized hazard and operability analysis (HAZOP)
report, while the data-driven method combines the autoregressive integrated
moving average model (ARIMA) and Granger causality test, and the traceability
mechanism uses the time-based retrospective reasoning method. In the
case study, the practical application of the method is compared with
the experimental application in a real petrochemical plant. The results
show that this method helps to improve the accuracy of correct diagnosis
of the root cause of the alarm and can assist the operators in decision
making. Using this method, the root cause diagnosis of alarm can be
realized quickly and scientifically, and the probability of misjudgment
by operators can be reduced, which has a certain degree of scientificity.