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Analytic signal space partitioning and symbolic dynamic filtering for degradation monitoring of electric motors

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Abstract

This study presents an application of the recently reported theories of analytic signal space partitioning (ASSP) and symbolic dynamic filtering (SDF) to address degradation monitoring in permanent magnet synchronous motors (PMSM). An (experimentally validated) mathematical model of generic PMSM is chosen to monitor degradation/fault events on a simulation test bed; and the estimated parameter of health condition is observed to vary smoothly and monotonically with degradation in magnetization of the PMSM.

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References

  1. Subbu, A., Ray, A.: Space partitioning via Hilbert transform for symbolic time series analysis. Appl. Phys. Lett. 92(8), 084107–1 to 084107–3 (2008)

    Google Scholar 

  2. Ray A.: Symbolic dynamic analysis of complex systems for anomaly detection. Signal Process. 84(7), 1115–1130 (2004)

    Article  MATH  Google Scholar 

  3. Rao, C., Ray, A., Sarkar, S., Yasar, M.: Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns. Signal Image Video Process. (2008). doi:10.1007/s11760-008-0061-8

  4. Pillay P., Krishnan R.: Modeling, simulation, and analysis of permanent-magnet motor drives, Part I: The permanent-magnet synchronous motor drive. IEEE Trans Ind Appl 25(2), 265–273 (1989)

    Article  Google Scholar 

  5. Cohen L.: Time-frequency analysis. Prentice Hall PTR, Upper Saddle River (1995)

    Google Scholar 

  6. Rajagopalan V., Ray A.: Symbolic time series analysis via wavelet-based partitioning. Signal Process. 86(11), 3309–3320 (2006)

    Article  MATH  Google Scholar 

  7. Thelin P. Short circuit fault conditions of a buried PMSM investigated with FEM. In: NORPIE/2002. Stockholm: Sweden, (2002)

  8. Hendershot J., Miller T.: Design of Brushless Permanent-Magnet Motors. Oxford University Press, Oxford (1996)

    Google Scholar 

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Authors and Affiliations

  1. The Pennsylvania State University, University Park, USA

    Subhadeep Chakraborty, Asok Ray, Aparna Subbu & Eric Keller

Authors
  1. Subhadeep Chakraborty
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  2. Asok Ray
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  3. Aparna Subbu
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  4. Eric Keller
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Corresponding author

Correspondence to Asok Ray.

Additional information

This work has been supported in part by NASA under Cooperative Agreement No. NNC04GA49G and Contract No. NNC07QA08P.

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Chakraborty, S., Ray, A., Subbu, A. et al. Analytic signal space partitioning and symbolic dynamic filtering for degradation monitoring of electric motors. SIViP 4, 399–403 (2010). https://doi.org/10.1007/s11760-009-0133-4

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  • DOI: https://doi.org/10.1007/s11760-009-0133-4

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