A novel association rule mining method for the identification of rare functional dependencies in Complex Technical Infrastructures from alarm data

A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data

“…We consider the alarm database of (Antonello et al 2021b). It contains the alarm messages generated by the simulation of a CTI formed by N c =300 components, each of which can be in five mutually exclusive and exhaustive states D ∈ {1, 2, 3, 4, 5} corresponding to healthy ( D = 1), partially degraded ( D = 2) , degraded ( D = 4) , very degraded ( D = 4) and failed ( D = 5) conditions.…”

“…The results obtained by the proposed novelty-based MOEA have been compared with the results of the Apriori-based ARM algorithm proposed in Antonello et al (2021) and of the MOEA for ARM identification proposed in Antonello et al (2020). The Apriori-based ARM algorithm performs an exhaustive search among all the possible combinations of alarms but requires to set small values of the minimum support and minimum confidence thresholds (here chosen equal to 5 and 0.6, respectively) to identify all the rare FDEPs; otherwise, with larger values of these thresholds, it would not find them (Antonello et al 2021b). The MOEA for ARM identification evolves a population of 500 association rules encoded in binary chromosomes of 2 × M al bits and employs the novelty measure (Eq.…”

“…Setting the mutation probability equal to 1∕M al and the crossover probability equal to 0.8 (Anand et al 2009), an initial population of N pop = 500 individuals, encoded into chromosomes of M al bits, is evolved for 2000 generations obtaining a final set of 500 patterns of alarms in 641 s on an Intel core (TM) i7-4790 CPU@ 3.6 GHz, 16 GB RAM. The patterns describe FDEPs involving components of different systems, whose failures can cause a local malfunction to propagate across the CTI systems and sub-systems, and originate unexpected cascades of failures over vast geographic areas (Thacker et al 2017;Antonello et al 2021b). Table 6 reports a selection of five discovered patterns which have been considered as most representative example of novel and unknown chains of events by CERN experts.…”

Identification of Critical Components in the Complex Technical Infrastructure of the Large Hadron Collider Using Relief Feature Ranking and Support Vector Machines

“…The identification of components critical for the functionalities of industrial and manufacturing systems is of essential importance to detect bottlenecks to which consolidation efforts must be directed, aiming at reducing the probability of abnormal conditions, number of shutdowns and recovery time [1,2]. When the structure function of the system and the reliabilities of its components are known, components critical for the successful functionality of the system can be identified by traditional reliability and risk analysis methods by calculating the so-called component importance measures [3,4].…”

“…However, this is unattainable for CTIs, which are large-scale systems of systems composed of thousands of interdependent and interconnected components executing diverse functions and using distinct technologies, e.g., hydraulics, mechanics, and electronics [5,6]. Due to their topological complexity, their spatial distribution, and the distinctions in their functionalities and technologies, the systems of a CTI are designed and constructed independently, and then assembled relying on the direct physical interfaces and considering functional dependencies that only take into account the theoretical scenarios of operation [2,7]. Throughout the life of a CTI, its systems evolve over time to maintain their operation, improve their performance, expand their functions, etc.…”