TY - GEN
T1 - Alarm Flood Analysis by Hierarchical Clustering of the Probabilistic Dependency between Alarms
AU - Weis, Iris
AU - Kinghorst, Jakob
AU - Kroger, Thomas
AU - Pirehgalin, Mina Fahimi
AU - Vogel-Heuser, Birgit
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/24
Y1 - 2018/9/24
N2 - 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.
AB - 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.
KW - Alarm pattern detection
KW - Data-driven methods
KW - Hierarchical clustering
UR - http://www.scopus.com/inward/record.url?scp=85055522143&partnerID=8YFLogxK
U2 - 10.1109/INDIN.2018.8471973
DO - 10.1109/INDIN.2018.8471973
M3 - Conference contribution
AN - SCOPUS:85055522143
T3 - Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018
SP - 227
EP - 232
BT - Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Industrial Informatics, INDIN 2018
Y2 - 18 July 2018 through 20 July 2018
ER -