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

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

doi:10.1109/ACC.2009.5160253

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

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

Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

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 language	English (US)
Title of host publication	2009 American Control Conference, ACC 2009
Pages	204-209
Number of pages	6
DOIs	https://doi.org/10.1109/ACC.2009.5160253
State	Published - Nov 23 2009
Event	2009 American Control Conference, ACC 2009 - St. Louis, MO, United States Duration: Jun 10 2009 → Jun 12 2009

Other

Other	2009 American Control Conference, ACC 2009
Country/Territory	United States
City	St. Louis, MO
Period	6/10/09 → 6/12/09

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

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10.1109/ACC.2009.5160253

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title = "Data-driven estimation of multiple fault parameters in permanent magnet synchronous motors",

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.",

author = "Subhadeep Chakraborty and Chinmay Rao and Eric Keller and Asok Ray and Murat Yasar",

year = "2009",

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doi = "10.1109/ACC.2009.5160253",

language = "English (US)",

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T1 - Data-driven estimation of multiple fault parameters in permanent magnet synchronous motors

AU - Chakraborty, Subhadeep

AU - Rao, Chinmay

AU - Keller, Eric

AU - Ray, Asok

AU - Yasar, Murat

PY - 2009/11/23

Y1 - 2009/11/23

N2 - 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.

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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U2 - 10.1109/ACC.2009.5160253

DO - 10.1109/ACC.2009.5160253

M3 - Conference contribution

AN - SCOPUS:70449631390

SN - 9781424445240

SP - 204

EP - 209

BT - 2009 American Control Conference, ACC 2009

T2 - 2009 American Control Conference, ACC 2009

Y2 - 10 June 2009 through 12 June 2009

ER -

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

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