Pattern detection in alarm data aroused great interest in research activities in recent years. Reducing alarm floods, which arise of causal dependencies of equipment and their alarms in automated production systems, is aimed to decrease alarm rates and aggregate information to valuable notifications for the operator. However, common alarm flood analysis often lacks robustness against random occurring alarms or interfering alarm patterns, which disturb the known structure of sequences. Therefore, this contribution introduce a preprocessing step, calculating the dependencies of alarms probabilistically. This approach meet the fuzziness of alarm systems regarding precision in time domain and interfering alarm signals. The results, based on two different industrial data sets, reveal high accurateness of the clusters defined by the proposed method. Alarm patterns can be detected even though the sequences are interrupted and interfered by further alarms or further causal dependent alarm floods.     «

Pattern detection in alarm data aroused great interest in research activities in recent years. Reducing alarm floods, which arise of causal dependencies of equipment and their alarms in automated production systems, is aimed to decrease alarm rates and aggregate information to valuable notifications for the operator. However, common alarm flood analysis often lacks robustness against random occurring alarms or interfering alarm patterns, which disturb the known structu...     »