1 / 23

Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis

Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis. Donatella Zappalà, Peter J. Tavner, Christopher J. Crabtree Durham University, UK Shuangwen Sheng NREL - National Wind Technology Center, Golden, Colorado EWEA 2013 7 th February 2013.

melosa
Download Presentation

Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis Donatella Zappalà, Peter J. Tavner, Christopher J. Crabtree Durham University, UK Shuangwen Sheng NREL - National Wind Technology Center, Golden, Colorado EWEA 2013 7th February 2013

  2. Wind Turbine Gearbox • Gearboxes fail to meet 20‐year design life • Premature failure increases O&M costs Cost of Energy (CoE) • Turbine downtime • Unplanned maintenance • ONSHORE: gearbox has one of the highest downtimes per failure • OFFSHORE: increased downtime • Complex logistics • Technical repairs • Weather windows Siemens press picture

  3. Automation of Condition Monitoring • Timely detection and diagnosis of gear defects essential • - Minimise unplanned downtime • Reliable and cost-effective condition monitoring systems (CMS) • - Plan maintenance activities more effectively • - Reduce O&M costs Reduce CoE • Current vibration-based CMSs mainly use FFT analysis • - Large amounts of data • - Costly and time-consuming manual analysis • - Frequent false alarms AUTOMATE data interpretation IMPROVE diagnostic accuracy and reliability

  4. Wind Turbine Condition Monitoring Test Rig

  5. Tests: Gearbox Gear Tooth Damage Investigate the progression of a High Speed Shaft Pinion tooth defect on the gearbox vibration signature at variable-speed and generator load Healthy Tooth Early Stages of Tooth Wear Missing Tooth

  6. 30kW Gearbox Vibration Signature Accelerometer 2nd Harmonic of HSS Mesh Frequency (2Xfmesh,HS) • 1560 rev/min HSS speed • 51% maximum generator output

  7. 30kW Gearbox Vibration Signature Accelerometer 2nd Harmonic of HSS Mesh Frequency (2Xfmesh,HS) • 1560 rev/min HSS speed • 51% maximum generator output

  8. 30kW Gearbox Vibration Signature Accelerometer 2nd Harmonic of HSS Mesh Frequency (2Xfmesh,HS) • 1560 rev/min HSS speed • 51% maximum generator output

  9. 30kW Gearbox Vibration Signature Accelerometer 2nd Harmonic of HSS Mesh Frequency (2Xfmesh,HS) Modulation by HSS Speed Modulation by HSS Speed Indication of severe damage on the HS Pinion

  10. Sideband Power Factor algorithm Track the overall power of the spectra 2Xfmesh,HSsideband narrowband

  11. SBPF vs. Fault Level - 30kW Gearbox SBPF = 0.0029e0.0433*P R² = 0.7502

  12. SBPF vs. Fault Level - 30kW Gearbox SBPF = 0.0057e0.0437*P R² = 0.8974 SBPF = 0.0029e0.0433*P R² = 0.7502

  13. SBPF vs. Fault Level - 30kW Gearbox SBPF = 0.013e0.042*P R² = 0.8808 SBPF = 0.0057e0.0437*P R² = 0.8974 SBPF = 0.0029e0.0433*P R² = 0.7502

  14. Detection Sensitivity - 30kW Gearbox Mean %SBPF = 320% Mean %SBPF = 100%

  15. NREL 750kW Gearbox Data source: Wind Turbine Gearbox Condition Monitoring Round Robin project Photo by Lee Jay Fingersh / NREL 16913 750kW Gearbox Damaged Gearbox: Completed dynamometer run-in test Field test: experienced two oil losses Stopped field test Retested in the dynamometer under controlled conditions Photo by GEARTECH, NREL / 19743 HSS Pinion

  16. 750kW Gearbox Vibration Signature • Available dataset: 1800 rev/min HSS speed and 50% rated power • Healthy Gearbox: one FFT spectrum (baseline) • Faulty Gearbox: 10 minutes raw vibration data 2nd Harmonic of HSS Mesh Frequency (2Xfmesh,HS)

  17. 750kW Gearbox Vibration Signature • Available dataset: 1800 rev/min HSS speed and 50% rated power • Healthy Gearbox: one FFT spectrum (baseline) • Faulty Gearbox: 10 minutes raw vibration data 2nd Harmonic of HSS Mesh Frequency (2Xfmesh,HS) Photo by GEARTECH, NREL / 19743

  18. 750kW Gearbox Vibration Signature • Available dataset: 1800 rev/min HSS speed and 50% rated power • Healthy Gearbox: one FFT spectrum (baseline) • Faulty Gearbox: 10 minutes raw vibration data 2nd Harmonic of HSS Mesh Frequency (2Xfmesh,HS) Photo by GEARTECH, NREL / 19743 Indication of damage on the HS Pinion Modulation by HSS Speed Modulation by HSS Speed

  19. SBPF - 750kW Wind Turbine Gearbox

  20. SBPF - 750kW Wind Turbine Gearbox Mean SBPF = 0.025 (gP2) Photo by GEARTECH, NREL / 19743

  21. Detection Sensitivity - 750kW Gearbox Mean %SBPF = 1251%

  22. Conclusions • SBPF algorithm proved successful for automatic gear damage detection and diagnosis within the Durham 30kW test rig gearbox • - 100% detection sensitivity for early stages of tooth wear • - 320% detection sensitivity for missing tooth • SBPF successfully tested on NREL 750kW gearbox dataset • - 1251% detection sensitivity • Simple to implement into commercial WT CMSs • - low risk of false alarms • Easily adaptable to all the WT gearbox parallel stages • - further investigation needed for planetary stages • SBPF trends and magnitude thresholds may indicate when a maintenance action needs to be performed

  23. Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis Thank you for your attention Donatella Zappalà donatella.zappala@durham.ac.uk This work is funded as part of the UK EPSRC Supergen Wind Energy Technologies programme, EP/H018662/1.

More Related