detecting wt gearbox failures – using condition monitoring or scada signals
DESCRIPTION
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). Contents. Motivation - PowerPoint PPT PresentationTRANSCRIPT
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)
Contents• Motivation• Method and Result using Condition Monitoring Signals• Method and Result using SCADA Signals• Conclusions
WT Reliability & Downtime
Reliability and downtime from more than 80000 turbine years extracted by ISET & TU Delft.
WT Gearbox Reliability & Downtime
Reliability and downtime from more than 80000 turbine years extracted by ISET & TU Delft.
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
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
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
Operational signals
Fun
ctio
nal
Sig
nals
0
0.1
0.2
0.3
0.4
0.5
0.6
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
Period: 400 - 500 MWh
0
0.1
0.2
0.3
0.4
0.5
0.6
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
Period: 100 - 200 MWh
0
0.1
0.2
0.3
0.4
0.5
0.6
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
Period: 600 - 700 MWh
0
0.1
0.2
0.3
0.4
0.5
0.6
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
Period: 800 - 900 MWh
Vibration / Load Characteristics
Bearing damage begins
Damage worsens
Serious deterioration and reduced vibration transmission path
Characteristic following bearing replacement
Vibration/Energy, Oil Debris/Energy
Enveloped Gearbox (High Speed End) Axial Vibration against Cumulative Energy Generated
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 100 200 300 400 500 600 700 800 900
Energy Generated (MWh)
Vib
rati
on
(g
)
A B
X
01/0
8/2
008
01/0
9/2
008
01/1
0/2
008
• 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
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
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
Rotorωr
Rotorωr
GearboxT gearoil ,T hss brg
GearboxT gearoil ,T hss brg
Generator ωg , Pout
Generator ωg , Pout
Wind speed Vel
SCADA Signal Modelling of Gearbox For Gears or Bearings
• Heat into Gear or Bearing proportional to work done on them, Q W T
• W= ₂ I⅟ xx2
• If efficiency of the Gear or Bearing is x
• Energy dissipated will be transferred as heat into the Gear or Bearing
• ₂ ⅟ Ixx2 (1-x)=kxTx
• Therefore 1-x = 2kxTx / Ixx2
• Gear or Bearing Inefficiency is proportional to Tx /x2
Tx /x2 is potential Detection Algorithm for Gear or Bearing damage
• For Gear or Bearing, Tx stands for Tgearoil and Thss brg; x stands for r and g, respectively.
• For bearings, that is Thss brg /g2
• For gears, that is Tgearoil /r2
SCADA Signal & Fault Analysis Gearbox Failure Detection Case:Planetary Stage Teeth Flaking Maintenance
1 month after3 months3 months3 monthsA B C D
Power Curve
SCADA Signal & Fault Analysis Gearbox Failure Detection Case:Planetary Stage Teeth Flaking Maintenance
3 months after3 months3 months3 monthsA B C D
Gearbox Gear or Bearing Detection Algorithm ΔTgearoil/ωr2
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
Dr. Yanhui Feng: [email protected]
Prof. Peter Tavner: [email protected]
ReliaWind: www.reliawind.eu
Supergen Wind: www.supergen-wind.org.uk
Thank you for attention!