Detecting WT Gearbox Failures – using Condition Monitoring or SCADA Signals

1. Detecting WT Gearbox Failures– using Condition Monitoring or SCADA Signals Dr. Yanhui Feng Dr. Yingning Qiu, Christopher Crabtree, Prof. Peter Tavner • Works are supported by • EU FP7 ReliaWind Project • UK SuperGen-Wind project (Phase I)

2. Contents • Motivation • Method and Result using Condition Monitoring Signals • Method and Result using SCADA Signals • Conclusions

3. WT Reliability & Downtime Reliability and downtime from more than 80000 turbine years extracted by ISET & TU Delft.

4. WT Gearbox Reliability & Downtime Reliability and downtime from more than 80000 turbine years extracted by ISET & TU Delft.

5. Offshore Challenges • Move to offshore environment • Larger machines • More hostile operating environment • Higher mechanical loading • Reduced accessibility • Many small failures lead to high maintenance costs

6. Detecting Incipient WT Gearbox Failure – Case Study using Condition Monitoring Signals Christopher Crabtree Dr. Yanhui Feng Prof. Peter Tavner Work mainly done in UK SuperGen-Wind: Phase I project

7. The WT and CM signals • Two speed, active stall machine with SKF WindCon condition monitoring system • Operational Signals • Wind speed • Load • Energy Generated • Generator Speed • Functional signals • Vibration (accelerometer) signals • 2 x main bearing • 4 x gearbox housing/bearings • 2 x generator bearings • Gearbox oil debris particle counts • Collect segments of data before the incident for off-line study Functional Signals Operational signals

8. Vibration / Load Characteristics Damage worsens Bearing damage begins Serious deterioration and reduced vibration transmission path Characteristic following bearing replacement

9. Vibration/Energy, Oil Debris/Energy • Period A: Steady increase in bearing damage • Vibration increases • Steady increase in rate of oil debris particle generation • Period B: Serious bearing deterioration • Vibration decreases as vibration transmission path deteriorates • Greater increase in oil debris particle generation • Point X: Bearing replacement

10. Detecting WT Gearbox Failure – Case Study using SCADA Signals Dr. Yingning Qiu Dr. Yanhui Feng Prof. Peter Tavner Work mainly done in EU FP7 ReliaWind project

11. Required SCADA signals • Operational signals • Generator power output Pout • Turbine rotor speed ωr • Generator speed ωg • Wind speed Vel • Functional signals • Nacelle temperature Tnacelle • Gearbox oil temperature T gearoil • Gearbox high speed shaft bearing temperature T hss brg Rotor ωr Gearbox T gearoil , T hssbrg Generatorωg , Pout • Wind speed Vel

12. SCADA Signal Modelling of Gearbox For Gears or Bearings • Heat into Gear or Bearing proportional to work done on them, Q a W aDT • W=⅟₂ Ixwx2 • If efficiency of the Gear or Bearing is hx • Energy dissipated will be transferred as heat into the Gear or Bearing • ⅟₂ Ixwx2 (1-hx)=kxDTx • Therefore 1-hx = 2kxDTx / Ixwx2 • Gear or Bearing Inefficiency is proportional to DTx /wx2 DTx /wx2 is potential Detection Algorithm for Gear or Bearing damage • For Gear or Bearing, DTxstands forDTgearoilandDThss brg; wxstands for wrand wg, respectively. • For bearings, that is DThss brg /wg2 • For gears, that is DTgearoil /wr2

13. SCADA Signal & Fault Analysis Gearbox Failure Detection Case:Planetary Stage Teeth Flaking Maintenance A C B D 1 month after 3 months 3 months 3 months Power Curve

14. SCADA Signal & Fault Analysis Gearbox Failure Detection Case:Planetary Stage Teeth Flaking Maintenance A C B D 3 months after 3 months 3 months 3 months Gearbox Gear or Bearing Detection Algorithm ΔTgearoil/ωr2

15. Conclusions A multi-parameter method is proposed for analysis of condition monitoring signals Comparison of independent monitoring signals against an operational signals gives early detection of incipient gearbox damage A multi-parameter severity factor could reduce false alarms and increase confidence in alarm signals Initial results show SCADA signals can be used for gearbox failure detection but we need to check whether they are sensitive to incipient failure modes Future work The method could be programmed into a commercial CMS Test on different gearboxes and fault Develop a severity factor Test on operational data before the event

16. Thank you for attention! Dr. Yanhui Feng: yanhui.feng@durham.ac.uk Prof. Peter Tavner: peter.tavner@durham.ac.uk ReliaWind: www.reliawind.eu Supergen Wind: www.supergen-wind.org.uk