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

Subhadeep Chakraborty; Asok Ray; Aparna Subbu; Eric Keller

doi:10.1007/s11760-009-0133-4

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

Subhadeep Chakraborty
, Asok Ray
, Aparna Subbu
, Eric Keller

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

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 language	English (US)
Pages (from-to)	399-403
Number of pages	5
Journal	Signal, Image and Video Processing
Volume	4
Issue number	4
DOIs	https://doi.org/10.1007/s11760-009-0133-4
State	Published - Nov 2010

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering

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10.1007/s11760-009-0133-4

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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.",

author = "Subhadeep Chakraborty and Asok Ray and Aparna Subbu and Eric Keller",

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AU - Chakraborty, Subhadeep

AU - Ray, Asok

AU - Subbu, Aparna

AU - Keller, Eric

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PY - 2010/11

Y1 - 2010/11

N2 - 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.

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

Abstract

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