d. j. lekou , f. mouzakis centre for renewable energy sources (cres)
DESCRIPTION
Fused Acoustic Emission & Vibration Techniques for Health Monitoring of Wind Turbine Gearboxes and Bearings. D. J. Lekou , F. Mouzakis Centre for Renewable Energy Sources (CRES). A. A. Anastassopoulos, D. Kourousis Envirocoustics S. A. Drive Train. Main Shaft Gearbox High speed shaft - PowerPoint PPT PresentationTRANSCRIPT
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.
European Wind Energy Conference 2009, Marseille, France 2
Drive Train
Main Shaft Gearbox High speed shaft Respective bearings
GeneratorRotor Hub
Main shaft
Bed PlateTorque Arm
GearboxBrake
Coupling
Main shaft bearing
European Wind Energy Conference 2009, Marseille, France 3
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
European Wind Energy Conference 2009, Marseille, France 7
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
European Wind Energy Conference 2009, Marseille, France 8
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)