Abstract
In this work, a Multi-Objective Evolutionary Algorithm (MOEA) is developed to identify Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) from alarm data. The objectives of the search are the maximization of a measure of novelty, which drives the exploration of the solution space avoiding to get trapped in local optima, and of a measure of dependency among alarms, which drives the uncovering of functional dependencies. The main contribution of the work is the direct identification of patterns of dependent alarms; this avoids going through the preliminary step of mining association rules, as typically done by state-of-the-art methods which, however, fail to identify rare functional dependencies due to the need of setting a balanced minimum occurrence threshold. The proposed framework for FDEPs identification is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected at the CTI of CERN (European Organization for Nuclear Research). The obtained results show that the framework enables the thorough exploration of the solution space and captures also rare functional dependencies.
Highlights
The identification of Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) has gained interest in the last years (Billinton and Allan 1992; Zio 2016; Serio et al 2018; Rebello et al 2018; Hickford et al 2018; Antonello et al 2019; Cantelmi et al 2021)
Datadriven methods for the identification of FEDPs in CTIs using alarm data have been developed (Serio et al 2018; Antonello et al 2019; Antonello et al 2021a). They are based on the application of the Association Rule Mining (ARM) (Agrawal and Imieliński 1993; Srikant and Agrawal 1996; Witten and Frank 2016) algorithm for scanning the alarm databases and identifying associations among patterns of alarms in the form of “if” rules; from these, the FDEPs are derived
The analysis reported in the same work shows that no spurious alarms are identified using values of in the range [0.01; 0.08]
Summary
The identification of Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) has gained interest in the last years (Billinton and Allan 1992; Zio 2016; Serio et al 2018; Rebello et al 2018; Hickford et al 2018; Antonello et al 2019; Cantelmi et al 2021). General guidelines and conceptual definitions have been provided in Zio (2016) In this context, datadriven methods for the identification of FEDPs in CTIs using alarm data have been developed (Serio et al 2018; Antonello et al 2019; Antonello et al 2021a). Datadriven methods for the identification of FEDPs in CTIs using alarm data have been developed (Serio et al 2018; Antonello et al 2019; Antonello et al 2021a) They are based on the application of the Association Rule Mining (ARM) (Agrawal and Imieliński 1993; Srikant and Agrawal 1996; Witten and Frank 2016) algorithm for scanning the alarm databases and identifying associations among patterns of alarms in the form of “if (antecedent) (consequent)” rules; from these, the FDEPs are derived. A modified version of the quicksort algorithm has been developed in Antonello et al (2020a) for the identification of the causal sequence of malfunctions from the probabilistic analysis of the temporal sequences of the alarms
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