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

Subhadeep Chakraborty, Asok Ray, Aparna Subbu, Eric Keller

Research output: Contribution to journalArticle

8 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)399-403
Number of pages5
JournalSignal, Image and Video Processing
Volume4
Issue number4
DOIs
StatePublished - Nov 1 2010

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Electric motors
Synchronous motors
Permanent magnets
Degradation
Monitoring
Magnetization
Health
Mathematical models

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

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Analytic signal space partitioning and symbolic dynamic filtering for degradation monitoring of electric motors. / Chakraborty, Subhadeep; Ray, Asok; Subbu, Aparna; Keller, Eric.

In: Signal, Image and Video Processing, Vol. 4, No. 4, 01.11.2010, p. 399-403.

Research output: Contribution to journalArticle

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