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Online intelligent condition monitoring of electrical machines.

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

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Online intelligent condition monitoring of electrical machines. / Ma, Xiandong.
2011. 223-228 Paper presented at The 24th International Congress on Condition Monitoring and Diagnostics Engineering Management (COMADEM2011), Stavanger, Norway.

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

Harvard

Ma, X 2011, 'Online intelligent condition monitoring of electrical machines.', Paper presented at The 24th International Congress on Condition Monitoring and Diagnostics Engineering Management (COMADEM2011), Stavanger, Norway, 30/05/11 - 1/06/11 pp. 223-228. <http://www.comadem2011.org/>

APA

Ma, X. (2011). Online intelligent condition monitoring of electrical machines.. 223-228. Paper presented at The 24th International Congress on Condition Monitoring and Diagnostics Engineering Management (COMADEM2011), Stavanger, Norway. http://www.comadem2011.org/

Vancouver

Ma X. Online intelligent condition monitoring of electrical machines.. 2011. Paper presented at The 24th International Congress on Condition Monitoring and Diagnostics Engineering Management (COMADEM2011), Stavanger, Norway.

Author

Ma, Xiandong. / Online intelligent condition monitoring of electrical machines. Paper presented at The 24th International Congress on Condition Monitoring and Diagnostics Engineering Management (COMADEM2011), Stavanger, Norway.6 p.

Bibtex

@conference{b26c3173aeef449bb46e23f2989dd07e,
title = "Online intelligent condition monitoring of electrical machines.",
abstract = "Condition monitoring needs smart technologies to diagnose faults and prognose failures, which this paper will investigate. The paper starts with the description of the fundamentals of system identification and neural network approaches. The proposed models will be tested and validated with measurement data. Issues related specially to continuous online monitoring will be addressed for these models. The work demonstrates that the proposed techniques can be potentially applied to online condition monitoring and therefore health assessment of power plant generators.",
keywords = "System identification, artificial neural network (ANN), condition monitoring, generator, power plant",
author = "Xiandong Ma",
note = "Conference name: The 24th International Congress on Condition Monitoring and Diagnostics Engineering Management (COMADEM2011), Stavanger, Norway, 30th May - 1st June 2011; The 24th International Congress on Condition Monitoring and Diagnostics Engineering Management (COMADEM2011) ; Conference date: 30-05-2011 Through 01-06-2011",
year = "2011",
month = may,
language = "English",
pages = "223--228",

}

RIS

TY - CONF

T1 - Online intelligent condition monitoring of electrical machines.

AU - Ma, Xiandong

N1 - Conference name: The 24th International Congress on Condition Monitoring and Diagnostics Engineering Management (COMADEM2011), Stavanger, Norway, 30th May - 1st June 2011

PY - 2011/5

Y1 - 2011/5

N2 - Condition monitoring needs smart technologies to diagnose faults and prognose failures, which this paper will investigate. The paper starts with the description of the fundamentals of system identification and neural network approaches. The proposed models will be tested and validated with measurement data. Issues related specially to continuous online monitoring will be addressed for these models. The work demonstrates that the proposed techniques can be potentially applied to online condition monitoring and therefore health assessment of power plant generators.

AB - Condition monitoring needs smart technologies to diagnose faults and prognose failures, which this paper will investigate. The paper starts with the description of the fundamentals of system identification and neural network approaches. The proposed models will be tested and validated with measurement data. Issues related specially to continuous online monitoring will be addressed for these models. The work demonstrates that the proposed techniques can be potentially applied to online condition monitoring and therefore health assessment of power plant generators.

KW - System identification

KW - artificial neural network (ANN)

KW - condition monitoring

KW - generator

KW - power plant

M3 - Conference paper

SP - 223

EP - 228

T2 - The 24th International Congress on Condition Monitoring and Diagnostics Engineering Management (COMADEM2011)

Y2 - 30 May 2011 through 1 June 2011

ER -