Data driven anomaly detection via symbolic identification of complex dynamical systems

Subhadeep Chakraborty, Eric Keller, Asok Ray

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

    2 Citations (Scopus)

    Abstract

    Some of the critical and practical issues regarding the problem of health monitoring of multi-component human-engineered systems have been discussed, and a syntactic method has been proposed. The method involves abstraction of a qualitative description from a general dynamical system structure, using state space embedding of the output data-stream and discretization of the resultant pseudo state and input spaces. The system identification is achieved through grammatical inference techniques, and the deviation of the plant output from the nominal estimated language gives a measure of anomaly in the system. The technique is validated on an experimental test-bed of a permanent magnet synchronous motor undergoing a gradual degradation of the encoder orientation feedback.

    Original languageEnglish (US)
    Title of host publicationProceedings 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
    Pages3745-3750
    Number of pages6
    DOIs
    StatePublished - 2009
    Event2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009 - San Antonio, TX, United States
    Duration: Oct 11 2009Oct 14 2009

    Other

    Other2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
    CountryUnited States
    CitySan Antonio, TX
    Period10/11/0910/14/09

    Fingerprint

    Synchronous motors
    Syntactics
    Permanent magnets
    Identification (control systems)
    Dynamical systems
    Health
    Feedback
    Degradation
    Monitoring

    All Science Journal Classification (ASJC) codes

    • Human-Computer Interaction
    • Control and Systems Engineering
    • Electrical and Electronic Engineering

    Cite this

    Chakraborty, S., Keller, E., & Ray, A. (2009). Data driven anomaly detection via symbolic identification of complex dynamical systems. In Proceedings 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009 (pp. 3745-3750). [5346890] https://doi.org/10.1109/ICSMC.2009.5346890
    Chakraborty, Subhadeep ; Keller, Eric ; Ray, Asok. / Data driven anomaly detection via symbolic identification of complex dynamical systems. Proceedings 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009. 2009. pp. 3745-3750
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    Chakraborty, S, Keller, E & Ray, A 2009, Data driven anomaly detection via symbolic identification of complex dynamical systems. in Proceedings 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009., 5346890, pp. 3745-3750, 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009, San Antonio, TX, United States, 10/11/09. https://doi.org/10.1109/ICSMC.2009.5346890

    Data driven anomaly detection via symbolic identification of complex dynamical systems. / Chakraborty, Subhadeep; Keller, Eric; Ray, Asok.

    Proceedings 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009. 2009. p. 3745-3750 5346890.

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

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    Chakraborty S, Keller E, Ray A. Data driven anomaly detection via symbolic identification of complex dynamical systems. In Proceedings 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009. 2009. p. 3745-3750. 5346890 https://doi.org/10.1109/ICSMC.2009.5346890