Wind turbine gearbox vibration signal signature and fault development through time

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

This paper aims to present a methodology for health monitoring wind turbine gearboxes using vibration data. Monitoring of wind turbines is a crucial aspect of maintenance optimisation that is required for wind farms to remain sustainable and profitable. The proposed methodology performs spectral line analysis and extracts health features from harmonic vibration spectra, at various time instants prior to a gear tooth failure. For this, the tachometer signal of the shaft is used to reconstruct the signal in the angular domain. The diagnosis approach is applied to detect gear faults affecting the intermediate stage of the gearbox. The health features extracted show the gradient deterioration of the gear at progressive time instants before the catastrophic failure. A classification model is trained for fault recognition and prognosis of time before failure. The effectiveness of the proposed fault diagnostic and prognostic approach has been tested with industrial data. The above will lay the groundwork of a robust framework for the early automatic detection of emerging gearbox faults. This will lead to minimisation of wind turbine downtime and increased revenue through operational enhancement.

LanguageEnglish
Title of host publication25th European Signal Processing Conference, EUSIPCO 2017
Place of Publicationpiscataway, N.J.
PublisherIEEE
Pages1380-1384
Number of pages5
ISBN (Electronic)9780992862671
DOIs
Publication statusPublished - 23 Oct 2017
Event25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece
Duration: 28 Aug 20172 Sep 2017

Conference

Conference25th European Signal Processing Conference, EUSIPCO 2017
CountryGreece
CityKos
Period28/08/172/09/17

Fingerprint

Wind turbines
Health
Gears
Tachometers
Monitoring
Gear teeth
Farms
Deterioration

Keywords

  • wind turbines
  • vibrations
  • amplitude modulation
  • shafts
  • gears

Cite this

Koukoura, Sofia ; Carroll, James ; Weiss, Stepha ; McDonald, Alasdair. / Wind turbine gearbox vibration signal signature and fault development through time. 25th European Signal Processing Conference, EUSIPCO 2017. piscataway, N.J. : IEEE, 2017. pp. 1380-1384
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abstract = "This paper aims to present a methodology for health monitoring wind turbine gearboxes using vibration data. Monitoring of wind turbines is a crucial aspect of maintenance optimisation that is required for wind farms to remain sustainable and profitable. The proposed methodology performs spectral line analysis and extracts health features from harmonic vibration spectra, at various time instants prior to a gear tooth failure. For this, the tachometer signal of the shaft is used to reconstruct the signal in the angular domain. The diagnosis approach is applied to detect gear faults affecting the intermediate stage of the gearbox. The health features extracted show the gradient deterioration of the gear at progressive time instants before the catastrophic failure. A classification model is trained for fault recognition and prognosis of time before failure. The effectiveness of the proposed fault diagnostic and prognostic approach has been tested with industrial data. The above will lay the groundwork of a robust framework for the early automatic detection of emerging gearbox faults. This will lead to minimisation of wind turbine downtime and increased revenue through operational enhancement.",
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Koukoura, S, Carroll, J, Weiss, S & McDonald, A 2017, Wind turbine gearbox vibration signal signature and fault development through time. in 25th European Signal Processing Conference, EUSIPCO 2017. IEEE, piscataway, N.J., pp. 1380-1384, 25th European Signal Processing Conference, EUSIPCO 2017, Kos, Greece, 28/08/17. https://doi.org/10.23919/EUSIPCO.2017.8081435

Wind turbine gearbox vibration signal signature and fault development through time. / Koukoura, Sofia; Carroll, James; Weiss, Stepha; McDonald, Alasdair.

25th European Signal Processing Conference, EUSIPCO 2017. piscataway, N.J. : IEEE, 2017. p. 1380-1384.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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