Symbolic identification and anomaly detection in complex dynamical systems

Subhadeep Chakraborty, Soumik Sarkar, Asok Ray

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    Symbolic dynamic filtering (SDF) has been reported in recent literature for early detection of anomalies (i.e., deviations from the nominal behavior) in complex dynamical systems. In this context, instead of solely relying on physics-based modeling that may be difficult to formulate and validate, this paper proposes data-driven modeling and system identification based on the concept of Symbolic Dynamics, Automata Theory, and Information Theory. For anomaly detection in inter-connected complex dynamical systems, with or without closed loop control, the input excitation to an individual component is likely to deviate from the nominal condition as a result of deterioration of some other component(s) or to accommodate disturbance rejection by feedback control actions. This paper presents a formal-language-based syntactic method of anomaly detection to account for deviations in the pertinent input excitation. A training algorithm is formulated to generate an automaton model of the underlying subsystem or component from a set of input-output combinations for different classes of inputs, where the objective is to detect (possibly gradually evolving) anomalies under different input conditions. The proposed method has been validated on a test apparatus of nonlinear active electronics.

    Original languageEnglish (US)
    Title of host publication2008 American Control Conference, ACC
    Pages2792-2797
    Number of pages6
    DOIs
    StatePublished - 2008
    Event2008 American Control Conference, ACC - Seattle, WA, United States
    Duration: Jun 11 2008Jun 13 2008

    Other

    Other2008 American Control Conference, ACC
    CountryUnited States
    CitySeattle, WA
    Period6/11/086/13/08

    Fingerprint

    Dynamical systems
    Automata theory
    Formal languages
    Disturbance rejection
    Information theory
    Syntactics
    Feedback control
    Deterioration
    Data structures
    Identification (control systems)
    Electronic equipment
    Physics

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

    Cite this

    Chakraborty, S., Sarkar, S., & Ray, A. (2008). Symbolic identification and anomaly detection in complex dynamical systems. In 2008 American Control Conference, ACC (pp. 2792-2797). [4586916] https://doi.org/10.1109/ACC.2008.4586916
    Chakraborty, Subhadeep ; Sarkar, Soumik ; Ray, Asok. / Symbolic identification and anomaly detection in complex dynamical systems. 2008 American Control Conference, ACC. 2008. pp. 2792-2797
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    Chakraborty, S, Sarkar, S & Ray, A 2008, Symbolic identification and anomaly detection in complex dynamical systems. in 2008 American Control Conference, ACC., 4586916, pp. 2792-2797, 2008 American Control Conference, ACC, Seattle, WA, United States, 6/11/08. https://doi.org/10.1109/ACC.2008.4586916

    Symbolic identification and anomaly detection in complex dynamical systems. / Chakraborty, Subhadeep; Sarkar, Soumik; Ray, Asok.

    2008 American Control Conference, ACC. 2008. p. 2792-2797 4586916.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    Chakraborty S, Sarkar S, Ray A. Symbolic identification and anomaly detection in complex dynamical systems. In 2008 American Control Conference, ACC. 2008. p. 2792-2797. 4586916 https://doi.org/10.1109/ACC.2008.4586916