Data-Driven Extraction of Association Rules of Dependent Abnormal Behaviour Groups

Antonello, Federico; Baraldi, Piero; Shokry, Ahmed; Zio, Enrico; Gentile, Ugo; Serio, L.

doi:10.3850/978-981-11-2724-3_0723-cd

Cited by 5 publications

(7 citation statements)

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“…We consider a CTI formed by p = 50 components, in which each component can be in five states, D ∈ {1, 2, 3, 4, 5} corresponding to healthy, partially degraded, degraded, very degraded and failed, respectively [11]. The components perform transitions among the states at random times.…”

Section: The 50-component Systemmentioning

confidence: 99%

“…The rankings of the components' criticalities obtained by the proposed data-driven importance measure are compared to those obtained by the Birnbaum importance measure on some simple systems, which are used to investigate the impact of the number of available data on the robustness of the ranking. Then, the proposed method is validated on two complex systems: an Auxiliary Feedwater (AFW) system of a nuclear power plant [10] and a 50-component system whose behavior is simulated to mimic the complexity of a CTI [11].…”

Section: Introductionmentioning

confidence: 99%

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A data-driven framework for identifying important components in complex systems

Baraldi

Zio

2020

Reliability Engineering & System Safety

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Complex technical infrastructures are systems of systems characterized by hierarchical structures, made by thousands of mutually interconnected components performing different functions. Given their complexity, it is difficult to derive their functional logic using traditional risk and reliability analysis methods based on engineering knowledge. In this work, we propose to address the problem in an innovative way that makes use of the large amount of data available from monitoring those systems. Specifically, we develop a data-driven framework to identify the critical components of a complex technical infrastructure. The criticality of a component with respect to the safe/failed state of the infrastructure is assessed considering a feature selection technique which employs Random Forest (RF) classification and a feature importance score. The proposed data-driven framework is applied to a nuclear power plant system and a synthetic case study, which mimics the complexity of a technical infrastructure.

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Section: The 50-component Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A data-driven framework for identifying important components in complex systems

Baraldi

Zio

2020

Reliability Engineering & System Safety

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“…A novel methodology [9] has been proposed to analyse large databases of alarms generated by the monitoring system and infer dependencies between systems and components to identify critical systems, mitigate and eliminate causes of downtime.…”

Section: 1systems and Components Dependencies: Lhc Point 8 Infrastructure Hidden Dependencies Analysismentioning

confidence: 99%

Machine Learning-Based System for the Availability and Reliability Assessment and Management of Critical Infrastructures (CASO)

Serio¹

2020

Proceedings of Artificial Intelligence for Science, Industry and Society — PoS(AISIS2019)

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A critical infrastructure is a complex interconnected system of systems providing basic and essential services to support the operation of particle accelerators but also industries and households for which they must guarantee high reliability of critical functions.Model-based approaches are usually adopted to provide an early identification of failures and to reveal hidden dependencies among subsystems. System models are complex and require constant updating to be reactive to system changes and real operating conditions, wear and aging. The interconnections between the different systems and the functional dependencies between their components are in many cases modified at both physical and functional levels while their degraded performances impact the overall system availability and reliability.A novel approach is proposed which combines model-based and Big Data analytics by machine learning techniques to extract descriptive and predictive models directly from data. The objective is to foresee and react in time to failures to reduce downtimes as well as to optimize maintenance and operation costs.The Computer-Aided System for critical infrastructure Operation (CASO) is designed to significantly and efficiently enhance the quality, safety, reliability and availability of critical infrastructures.We report on the design of CASO, its implementation and on the preliminary results inferred on historical and live stream data recorded from CERN's technical infrastructure. Proposal for the full deployment and expected long-term capabilities will also be discussed.

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“…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). Given the CTIs complexity and evolutionary behaviour, the identification of FDEPs by classical methods of system decomposition and logic analysis is quite unattainable (Zio 2016;Rebello et al 2018).…”

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confidence: 99%

“…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). 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) then (consequent)" rules; from these, the FDEPs are derived.…”

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confidence: 99%

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

et al. 2022

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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.

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Data-Driven Extraction of Association Rules of Dependent Abnormal Behaviour Groups

Cited by 5 publications

References 0 publications

A data-driven framework for identifying important components in complex systems

A data-driven framework for identifying important components in complex systems

Machine Learning-Based System for the Availability and Reliability Assessment and Management of Critical Infrastructures (CASO)

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

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