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.
| Original language | English |
|---|---|
| Title of host publication | 25th European Signal Processing Conference, EUSIPCO 2017 |
| Place of Publication | piscataway, N.J. |
| Publisher | IEEE |
| Pages | 1380-1384 |
| Number of pages | 5 |
| ISBN (Electronic) | 9780992862671 |
| DOIs | |
| Publication status | Published - 23 Oct 2017 |
| Event | 25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece Duration: 28 Aug 2017 → 2 Sep 2017 |
Conference
| Conference | 25th European Signal Processing Conference, EUSIPCO 2017 |
|---|---|
| Country | Greece |
| City | Kos |
| Period | 28/08/17 → 2/09/17 |
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Keywords
- wind turbines
- vibrations
- amplitude modulation
- shafts
- gears
Cite this
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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 proceeding › Conference contribution book
TY - GEN
T1 - Wind turbine gearbox vibration signal signature and fault development through time
AU - Koukoura, Sofia
AU - Carroll, James
AU - Weiss, Stepha
AU - McDonald, Alasdair
N1 - © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PY - 2017/10/23
Y1 - 2017/10/23
N2 - 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.
AB - 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.
KW - wind turbines
KW - vibrations
KW - amplitude modulation
KW - shafts
KW - gears
UR - http://www.scopus.com/inward/record.url?scp=85041461220&partnerID=8YFLogxK
UR - https://www.eusipco2017.org/
U2 - 10.23919/EUSIPCO.2017.8081435
DO - 10.23919/EUSIPCO.2017.8081435
M3 - Conference contribution book
SP - 1380
EP - 1384
BT - 25th European Signal Processing Conference, EUSIPCO 2017
PB - IEEE
CY - piscataway, N.J.
ER -