Alarm Correlation to improve industrial fault management

juillet 2020
Ingénierie & Outils numériques
Communications avec actes dans un congrès international
Auteurs : Mohamed Amin BENATIA (LINEACT), Anne LOUIS (LINEACT), David BAUDRY (LINEACT)
Conférence : IFAC World Congress 2020, 11 juillet 2020

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.