European Wind Energy Conference 2009

16 – 19 March, Marseille, France

Fused Acoustic Emission & Vibration

Techniques for Health Monitoring of

Wind Turbine Gearboxes and Bearings

D. J. Lekou, F. MouzakisCentre for Renewable Energy Sources

(CRES)A. A. Anastassopoulos, D.

KourousisEnvirocoustics S. A.

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Drive Train

Main Shaft Gearbox High speed shaft Respective bearings

GeneratorRotor Hub

Main shaft

Bed PlateTorque Arm

GearboxBrake

Coupling

Main shaft bearing

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Drive Train Component Failures

Roller Bearings Ring & Roller cracks Wearing, Spalling, Brinelling & Fluting

Gear Elements Tooth cracking, Micro-pitting Abrasion & Spalling

Shafts Cracks & Imbalances

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Condition Monitoring

Vibration measurements Wind Turbine Operation Measurements

Complemented by: Oil analysis Debris monitoring Temperature monitoring Visual Inspections (incl. Bore-scope)

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Vibration analysis - Classification

Low Frequency (0 – 20 kHz) Monitoring of total oscillatory motion FFT, Spectrum analysis

Medium Frequency (20 – 100 kHz) Detection of waves @ the speed of sound HFE, “Stress wave”, “Spike Energy”, etc.

High Frequency ( >100 kHz) Detection of transient elastic waves Acoustic Emission

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Vibration analysis – Low Frequency

Low Frequency (0 – 20 kHz) Monitoring of total oscillatory motion FFT, Spectrum analysis

Medium Frequency (20 – 100 kHz) Detection of waves @ the speed of sound HFE, “Stress wave”, “Spike Energy”, etc.

High Frequency ( >100 kHz) Detection of transient elastic waves Acoustic Emission

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Vibration analysis – Medium Frequency

Low Frequency (0 – 20 kHz) Monitoring of total oscillatory motion FFT, Spectrum analysis

Medium Frequency (20 – 100 kHz) Detection of waves @ the speed of sound HFE, “Stress wave”, “Spike Energy”, etc.

High Frequency ( >100 kHz) Detection of transient elastic waves Acoustic Emission

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Vibration analysis – High Frequency

Low Frequency (0 – 20 kHz) Monitoring of total oscillatory motion FFT, Spectrum analysis

Medium Frequency (20 – 100 kHz) Detection of waves @ the speed of sound HFE, “Stress wave”, “Spike Energy”, etc.

High Frequency ( >100 kHz) Detection of transient elastic waves Acoustic Emission

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Acoustic Emission

Detection of transient events Due to rapid release of energy Localized Source

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Acoustic Emission Signal & Features

Continuous type Time-Driven Data

RMS ASL Absolute Energy

Burst type AE Hit Descriptors e.g.

Amplitude Counts Energy

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Experimental Set-Up

Measuring Loads & Vibrations Wind Inflow parameters Wind Turbine Operational Parameters Mechanical Loads on Drive – Train Vibration & Displacement on Gearbox

Acoustic Emission System PAC PCI-2 18-bit A/D card CH1: R30a (100kHz – 400kHz) CH2: PICO (200kHz – 750kHz)

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Experimental Set-up details

AE #1

ACCEL

ACCEL

18

17

19

1

2

3

4

5

6

8

7

HSS

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Indicative Results – Simulated source

kHz

S

V

Magnit

ude

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Indicative Results – Operational AE Hit

kHz

S

V

Magnit

ude

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Indicative results – AE & Vibration

116.376 116.3765 116.377 116.3775 116.378

-0 .012

-0 .008

-0 .004

0

0.004

0.008

0.012

AE

CH

1 (V

)

T im e (s)

-1 .2

-0 .8

-0 .4

0

0.4

0.8

Acc

elar

atio

n (

V)

A cc #1

Acc #2

Acc #3

2ms

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Indicative Results – Time-driven data

Time (s)

Abso

lute

Energ

yA

SL

Pow

er

(kW

)

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Indicative results – e.g. ASL vs POWER

-200 0 200 400 600 800 1000Pow er (kW )

0

4

8

12

16

20

24

28

32

36

40

44

48

52

56

60

64

68

AS

L (c

hann

el 1

)

-200 0 200 400 600 800 1000Pow er (kW )

0

4

8

12

16

20

24

28

32

36

40

44

48

52

56

60

64

68

AS

L (c

han

nel

2)

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Conclusions - Discussion

Footprint of normal operation Capturing Time-driven data Capturing Transient events

Simulation of failure upper limit Different characteristics from “normal”

Both methods have limitations Combination of methods preferred

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Future Work

Footprint of failures Introduction of failures Use of Pulser Simulator

Increase data base Application on other Wind Turbine Types

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Thank you for your attention

Acknowledgements Partially supported by the European

Regional Development Fund and the Greek Secretariat for R & D through AKMON

Part of the work was performed within the EC co-funded project PROTEST (212825)