A methodology to extract temporal patterns of alarm sequences and operator actions from the log files of alarm management systems is proposed. Firstly, time-segments that are informative from the viewpoint of operator interventions are identified by the algorithm. These segments include series of alarms that initialize operator actions, sets of operator actions, and a period that potentially covers the effects of the corrective actions of the operators. In the second step of the methodology, the sets of operator actions that are frequently applied in the same situations are determined. For this purpose, the FP-Growth Algorithm, which is one of the fastest tools of frequent item-set mining and generates well-structured action trees that are not only suitable for the visualization of interventions but lend themselves to build association rules that could be directly applied in decision support systems, is utilized. Finally, multi-temporal sequence mining is applied to reveal what alarms led to the sets of operator actions and what were the effects of these interventions. The applicability of the methodology is illustrated by presenting results connected to the analysis of the delayed coker plant at the Danube Refinery of the MOL Group.