Fault analysis and condition monitoring of the wind turbine gearbox

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Author(s)

Detail(s)

Original languageEnglish
Article number6179983
Pages (from-to)526-535
Journal / PublicationIEEE Transactions on Energy Conversion
Volume27
Issue number2
Publication statusPublished - 2012
Externally publishedYes

Abstract

Data mining algorithms and statistical methods are applied to analyze the jerk data obtained from monitoring the gearbox of a wind turbine. Two types of analyses are performedfailure component identification and monitoring vibration excitement. In failure component identification, the failed stages of the gearbox are identified in time-domain analysis and frequency-domain analysis. In the time domain, correlation coefficient and clustering analysis are applied. The fast Fourier transformation with time windows is utilized to analyze the frequency data. To monitor the vibration excitement of the gearbox in its high-speed stage, data mining algorithms and statistical quality control theory are combined to develop a monitoring model. The capability of the monitoring model to detect changes in the gearbox vibration excitement is validated by the collected data. © 2012 IEEE.

Research Area(s)

  • Data mining, failure component identification, fast Fourier, jerk, transformation with time windows, vibration excitement monitoring, wind turbine gearbox

Citation Format(s)

Fault analysis and condition monitoring of the wind turbine gearbox. / Zhang, Zijun; Verma, Anoop; Kusiak, Andrew.

In: IEEE Transactions on Energy Conversion, Vol. 27, No. 2, 6179983, 2012, p. 526-535.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review