health monitoring of wind turbines - wichita

20
Health Monitoring of Wind Turbines Jim Steck Walter Horn Janet Twomey Zach Kral Seyed Niknam Morteza Assadi Gigi Phan Jordan Jensen Supported by the U.S. Department of Energy DOE DE-FG36-08GO88149

Upload: others

Post on 16-Nov-2021

0 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: Health Monitoring of Wind Turbines - Wichita

Health Monitoring of Wind Turbines

• Jim Steck

• Walter Horn

• Janet Twomey

• Zach Kral

• Seyed Niknam

• Morteza Assadi

• Gigi Phan

• Jordan Jensen Supported by the U.S. Department of EnergyDOE DE-FG36-08GO88149

Page 2: Health Monitoring of Wind Turbines - Wichita

GOALS

•Detect Wind Turbine Health

•Perform Maintenance only When Needed Condition Based Maintenance

•Reduce Costs and Downtime

•Predict Remaining Useful Life

Page 3: Health Monitoring of Wind Turbines - Wichita

MOTIVATION(not a new idea)

Page 4: Health Monitoring of Wind Turbines - Wichita

Space Flight

• NASA – Spacecraft Telemetry

• Astronaut – Health Monitoring

Page 5: Health Monitoring of Wind Turbines - Wichita

Vehicle Health Monitoring

• Navy – Ship systems monitoring

• DOD – Joint Strike Fighter

Page 6: Health Monitoring of Wind Turbines - Wichita

Aircraft Structural Health Monitoring

• Scheduled structural inspection

• Critical interior parts can cost $80,000 just to inspect

Hawker 800Cessna Citation

Page 7: Health Monitoring of Wind Turbines - Wichita

Active Corrosion Detection

Page 8: Health Monitoring of Wind Turbines - Wichita

Wind Turbine Bearings

• SKF WindCon 3.0 (www.skf.com)– Vibration monitoring for online condition monitoring– automatic lubrication

Page 9: Health Monitoring of Wind Turbines - Wichita

Diagnostics

Page 10: Health Monitoring of Wind Turbines - Wichita

TECHNOLOGIESFOR STRUCTURAL HEALTH

MONITORING

Page 11: Health Monitoring of Wind Turbines - Wichita

Acoustic Emission (Passive)• Physical Acoustics Corporation

(MISTRAS is parent company)

– Growing cracks emit PINGS

• Detect with acoustic sensors

• Filter to isolate crack pings

• Artificial Intelligence to predict crack growth and remaining useful life as it happens

Page 12: Health Monitoring of Wind Turbines - Wichita

Active Ultrasonic

• Acellent Corporation

– Emit acoustic pulses that travel through structure

– Detect baseline healthy structure

– Continually monitor and compare to detectdamage

Page 13: Health Monitoring of Wind Turbines - Wichita

CURRENT WORK

Page 14: Health Monitoring of Wind Turbines - Wichita

Crack Detection in Metals

• Acoustic emission and active ultrasonic

• Static pull of cracked test article to failure

Acoustic EmissionSensorsEdge Crack

Crack

Page 15: Health Monitoring of Wind Turbines - Wichita

Crack Length Prediction

1

10

100

1000

0 200 400 600 800 1000Time (sec)

NN

Est

imat

ion

of d

a (in

)

0

200

400

600

800

1000

1200

1400

Load

(lb)

ch1

ch2

Load

0 100 200 300 400 500 600 700 800 9000

0.2

0.4

0.6

0.8

Cra

ck le

ngth

(in)

Elapsed Time (sec)

Average Crack Length

0 100 200 300 400 500 600 700 800 900-500

0

500

1000

1500

Load

(lb)

Crack SizeLoading

Page 16: Health Monitoring of Wind Turbines - Wichita

Active Damage Detection

Artificial Intelligence to automate wave analysis with multiple sensors Damage

Detected

Baseline (no damage) With damage

Page 17: Health Monitoring of Wind Turbines - Wichita

Damage Detection in Composites

• Acoustic emission and active ultrasonic

• Material is more complex

– Fiber breakage

– Epoxy matrix failure

– Delamination

• AI tools for signal processing

• Determining failure location and severity

Page 18: Health Monitoring of Wind Turbines - Wichita

Ongoing Composite Testing

Page 19: Health Monitoring of Wind Turbines - Wichita

Bearing and Shaft Wear

• Acoustic Emission sensors and vibration sensors on bearing housing

• Bearing shaft test rig – Accelerated degradation

– High temperature

– High humidity

Page 20: Health Monitoring of Wind Turbines - Wichita

Future Tasks

• Investigate integrating sensors into the blade structure

• Identify appropriate sensors for in-servicemonitoring

• Develop system reliability model

• Develop diagnostic and prognostic tools