Alarm Correlation to improve industrial fault management

Actual alarm systems used in manufacturing applications lacks explanation and
indication of the root causes, which results in a poor decision making. In addition, manufacturing
systems are more and more complex, so relaying on human operators for alarm information
management becomes impossible. For this, a computerized tool to support human operators
(i.E., decision support for information management system) is needed and would increase
analytical capability for alarm analysis. To this e ect, we introduce in this paper an autonomous
data mining method to search historical alarm logs for the correlations that can represent
causal relationships, which can support alarm management and system improvement. We
investigate the use of Frequent Pattern Mining algorithm, an enumeration-tree based approach,
for extracting relationships and automatically detect correlation between industrial alarms. Due
to the time indexation of the alarm events, we adapt the algorithm in order to take into account
the duration between alarms when extracting the itemsets. Filtered rules where evaluated
according to the Minimmum support & Con dence framework. Obtained results show that FPM
algorithm can derive very useful knowledge on system behaviour allowing the identi cation of
alarm subsequence with the corresponding root cause.