Data-driven estimation of multiple fault parameters in permanent magnet synchronous motors

Subhadeep Chakraborty, Chinmay Rao, Eric Keller, Asok Ray, Murat Yasar

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

    1 Citation (Scopus)

    Abstract

    This paper presents symbolic analysis of time series data for estimation of multiple faults in permanent magnet synchronous motors (PMSM). The analysis is based on an experimentally validated dynamic model, where the flux linkage of the permanent magnet and friction in the motor bearings are varied in the simulation model to represent different stages of degradation. The fault magnitudes are estimated from the time series of the instantaneous line current. The behavior patterns of the PMSM are compactly generated as quasi-stationary state probability histograms associated with the finite state automata of its symbolic dynamic representation. The proposed fault estimation method is suitable for real-time execution on a limited-memory platforms, such as those used in sensor network nodes.

    Original languageEnglish (US)
    Title of host publication2009 American Control Conference, ACC 2009
    Pages204-209
    Number of pages6
    DOIs
    StatePublished - Nov 23 2009
    Event2009 American Control Conference, ACC 2009 - St. Louis, MO, United States
    Duration: Jun 10 2009Jun 12 2009

    Other

    Other2009 American Control Conference, ACC 2009
    CountryUnited States
    CitySt. Louis, MO
    Period6/10/096/12/09

    Fingerprint

    Synchronous motors
    Permanent magnets
    Time series
    Bearings (structural)
    Finite automata
    Sensor networks
    Dynamic models
    Friction
    Fluxes
    Data storage equipment
    Degradation

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

    Cite this

    Chakraborty, S., Rao, C., Keller, E., Ray, A., & Yasar, M. (2009). Data-driven estimation of multiple fault parameters in permanent magnet synchronous motors. In 2009 American Control Conference, ACC 2009 (pp. 204-209). [5160253] https://doi.org/10.1109/ACC.2009.5160253
    Chakraborty, Subhadeep ; Rao, Chinmay ; Keller, Eric ; Ray, Asok ; Yasar, Murat. / Data-driven estimation of multiple fault parameters in permanent magnet synchronous motors. 2009 American Control Conference, ACC 2009. 2009. pp. 204-209
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    abstract = "This paper presents symbolic analysis of time series data for estimation of multiple faults in permanent magnet synchronous motors (PMSM). The analysis is based on an experimentally validated dynamic model, where the flux linkage of the permanent magnet and friction in the motor bearings are varied in the simulation model to represent different stages of degradation. The fault magnitudes are estimated from the time series of the instantaneous line current. The behavior patterns of the PMSM are compactly generated as quasi-stationary state probability histograms associated with the finite state automata of its symbolic dynamic representation. The proposed fault estimation method is suitable for real-time execution on a limited-memory platforms, such as those used in sensor network nodes.",
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    Chakraborty, S, Rao, C, Keller, E, Ray, A & Yasar, M 2009, Data-driven estimation of multiple fault parameters in permanent magnet synchronous motors. in 2009 American Control Conference, ACC 2009., 5160253, pp. 204-209, 2009 American Control Conference, ACC 2009, St. Louis, MO, United States, 6/10/09. https://doi.org/10.1109/ACC.2009.5160253

    Data-driven estimation of multiple fault parameters in permanent magnet synchronous motors. / Chakraborty, Subhadeep; Rao, Chinmay; Keller, Eric; Ray, Asok; Yasar, Murat.

    2009 American Control Conference, ACC 2009. 2009. p. 204-209 5160253.

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

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    AB - This paper presents symbolic analysis of time series data for estimation of multiple faults in permanent magnet synchronous motors (PMSM). The analysis is based on an experimentally validated dynamic model, where the flux linkage of the permanent magnet and friction in the motor bearings are varied in the simulation model to represent different stages of degradation. The fault magnitudes are estimated from the time series of the instantaneous line current. The behavior patterns of the PMSM are compactly generated as quasi-stationary state probability histograms associated with the finite state automata of its symbolic dynamic representation. The proposed fault estimation method is suitable for real-time execution on a limited-memory platforms, such as those used in sensor network nodes.

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    Chakraborty S, Rao C, Keller E, Ray A, Yasar M. Data-driven estimation of multiple fault parameters in permanent magnet synchronous motors. In 2009 American Control Conference, ACC 2009. 2009. p. 204-209. 5160253 https://doi.org/10.1109/ACC.2009.5160253