Prognosis of failure precursor in complex electrical systems using symbolic dynamics

Ravindra Patankar, Venkatesh Rajagopalan, Devendra Tolani, Asok Ray, Michael Begin

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

    2 Citations (Scopus)

    Abstract

    Failures in a plant's electrical components are a major source of performance degradation and plant unavailability, In order to detect and monitor failure precursors and anomalies early in electrical systems, we have developed signal processing capabilities that can detect and map patterns in already existing and available signals to an anomaly measure. Toward this end, the language measure theory based on real analysis, finite state automaton, symbolic dynamics and information theory has been deployed. Application of this theory for electronic circuit failure precursor detection resulted in a robust statistical pattern recognition technique. This technique was observed to be superior to conventional pattern recognition techniques such as neural networks and principal component analysis for anomaly detection because it exploits a common physical fact underling most anomalies which conventional techniques do not. Symbolic dynamic technique resulted in a monotonically increasing smooth anomaly plot which was experimentally repeatable to a remarkable accuracy. For the Van der Pol oscillator circuit board experiment, this lead to consistently accurate predictions for the anomaly parameter and its range.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 2007 American Control Conference, ACC
    Pages1846-1851
    Number of pages6
    DOIs
    StatePublished - Dec 1 2007
    Event2007 American Control Conference, ACC - New York, NY, United States
    Duration: Jul 9 2007Jul 13 2007

    Other

    Other2007 American Control Conference, ACC
    CountryUnited States
    CityNew York, NY
    Period7/9/077/13/07

    Fingerprint

    Pattern recognition
    Networks (circuits)
    Information theory
    Finite automata
    Principal component analysis
    Signal processing
    Neural networks
    Degradation
    Experiments

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering

    Cite this

    Patankar, R., Rajagopalan, V., Tolani, D., Ray, A., & Begin, M. (2007). Prognosis of failure precursor in complex electrical systems using symbolic dynamics. In Proceedings of the 2007 American Control Conference, ACC (pp. 1846-1851). [4282219] https://doi.org/10.1109/ACC.2007.4282219
    Patankar, Ravindra ; Rajagopalan, Venkatesh ; Tolani, Devendra ; Ray, Asok ; Begin, Michael. / Prognosis of failure precursor in complex electrical systems using symbolic dynamics. Proceedings of the 2007 American Control Conference, ACC. 2007. pp. 1846-1851
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    Patankar, R, Rajagopalan, V, Tolani, D, Ray, A & Begin, M 2007, Prognosis of failure precursor in complex electrical systems using symbolic dynamics. in Proceedings of the 2007 American Control Conference, ACC., 4282219, pp. 1846-1851, 2007 American Control Conference, ACC, New York, NY, United States, 7/9/07. https://doi.org/10.1109/ACC.2007.4282219

    Prognosis of failure precursor in complex electrical systems using symbolic dynamics. / Patankar, Ravindra; Rajagopalan, Venkatesh; Tolani, Devendra; Ray, Asok; Begin, Michael.

    Proceedings of the 2007 American Control Conference, ACC. 2007. p. 1846-1851 4282219.

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

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    Patankar R, Rajagopalan V, Tolani D, Ray A, Begin M. Prognosis of failure precursor in complex electrical systems using symbolic dynamics. In Proceedings of the 2007 American Control Conference, ACC. 2007. p. 1846-1851. 4282219 https://doi.org/10.1109/ACC.2007.4282219