damage identification in wind turbine blades · • modal parameters of the lower modes are not the...
TRANSCRIPT
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Damage Identification in Wind Turbine Blades
2nd Annual Blade Inspection, Damage and Repair Forum, 2014
Martin Dalgaard Ulriksen Research Assistant, Aalborg University, Denmark
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Presentation outline
• Research motivation
• Basic principles of damage identification
– Identification levels
– Physical quantities typically used
• Vibration-based damage identification
– Measurement of vibrations
– Applicable vibration quantities
• Case study
• Conclusions
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Research motivation
Reliable damage identification enables, i.a., the turbine operators to:
• optimize maintenance
• shut down in case of an
emergency
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Research motivation - continued
Cracks Edge damages Surface and
coating damages
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Cracks and edge debondings are most critical damage types - require structural repairs.
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Basic principles of damage identification
As defined by A. Rytter, damage identification covers 4 accumulative steps:
1. Damage detection
2. Damage localization
3. Damage assessment
4. Damage consequence
Example with damage length L:
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Lvl. 2 Lvl. 3 Lvl. 2
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Basic principles of damage identification – cont.
Quantities typically used for damage identification:
• Temperature
• Noise
• Vibration
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Basic principles of damage identification – cont.
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Temperature-based (thermography)
Basic idea: use infrared thermography to detect subsurface anomalies on the basis of temperature differences on the investigated surface.
• Advantages:
• Characterization of stress distributions and identification of stress concentration areas of
a surface • Area investigating technique
• Disadvantages: • Sensitivity towards spatial and temporal
temperature variations • Local measurements to assess damages
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Basic principles of damage identification – cont.
Noise-based (acoustic emission)
Basic idea: monitor the acoustic emission generated by onset or growth of damage.
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• Advantages: • Identifying damage areas plus hot spots and weak points
• Disadvantages: • Relatively high acoustic energy
attenuation (diversity of materials)
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Basic principles of damage identification – cont.
Vibration-based
Basic idea: monitor the vibrations and examine signal anomalies.
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• Advantages: • Independent of structural material
• Disadvantages: • Sensitivity difference in modal parameters
for different damage types
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Basic principles of damage identification – cont.
Applicability of different methods for damage identification: Damage types: 1) Cracks, 2) Edge damages, 3) Surface and coating damages
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Vibration-based damage identification
Vibrations can be measured as, e.g., displacements, velocities, and accelerations. Common for wind turbines is to mount wire-less accelerometers.
Based on time-dependent accelerations, the so-called modal parameters can be extracted through Operational Modal Analysis (OMA).
• Eigenfrequencies
• Mode shapes
• Damping ratios (not suitable for damage identification)
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Vibration-based damage identification – cont.
• Eigenfrequencies (global parameter): – Natural frequencies of vibration for a system. Depends on the relation
between stiffness and mass of the system.
• Mode shapes (local parameter): – Relative motion between degrees of freedom when vibrating at
eigenfrequencies.
Beam system 1. mode 2. mode
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Vibration-based damage identification – cont.
Numerous damage identification methods utilizing eigen-frequencies and/or mode shapes have been proposed.
First, we examine methods based on direct comparison between pre- and post-damage eigenfrequencies and mode shapes to see why these are inapplicable. Subsequently, we look at a more sophisticated mode shape-based method.
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Case study
Damage identification in SSP 34 m wind turbine blade.
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Case study – continued
Measurements during approximately seven minutes, corresponding to at least 500 oscillations at the lowest frequency of interest (≈ 1.3 Hz).
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Only one cable for 1. Data 2. Synchronization 3. Power supply
Short accelerometer cable
Tri-axial accelero-meter mounted on swivel base
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Case study – continued
Introduction of a 1.2 m trailing edge debonding (3.5 % of blade length) by use of hammer and chisel. The debonding was initiated 18.8 m from the blade root.
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Case study – continued
Excited by hits with foam-wrapped wooden sticks at several locations along the blade (simulating ambient vibrations).
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Case study – continued
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OMA setup: • Unmeasured input: hits with foam-wrapped wooden sticks. • Measured output: accelerations in 20 points.
1.2 m debonding
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Case study – continued
Eigenfrequency findings:
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Natural frequencies, Hz
Diff.,% Undamaged Damaged
Mode Name Mean Confid.,% Mean Confid.,% 1 1st flap 1.36 0.79% 1.35 0.55% 0.48% 2 1st edge 1.86 0.47% 1.86 0.28% -0.10% 3 2nd flap 4.21 0.09% 4.21 0.16% 0.09% 4 2nd edge 7.12 0.04% 7.12 0.12% 0.11% 5 3rd flap 9.19 0.64% 9.17 0.13% 0.18% 6 1st torsion 12.40 0.18% 12.37 0.11% 0.24% 7 4th flap + 3rd edge 14.99 0.10% 14.98 0.09% 0.10%
The difference is much smaller than
the confidence!
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Case study – continued
Mode shape findings:
• No traces of the damage at the lowest modes
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1st flapwise mode 1st edgewise mode
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Case study – continued
Mode shape findings:
• No traces of the damage at the lowest modes
• Some differences occur in the higher modes
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8th mode (combination of flap and edge)
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Case study – continued
Direct comparisons of pre- and post-damage modal parameters do not facilitate valid damage identification. Therefore, continuous wavelet transformation (CWT) is employed.
CWT: Calculates similarity between a signal and a so-called wavelet function. Works as a discontinuity/irregularity scanner.
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Case study – continued
CWT results by use of 8th mode (combination of 3rd edgewise and 4th flapwise bending modes) and a 4th order Gaussian wavelet:
(a) CWT of post-damage signal-processed 8th mode shape. (b) CWT of pre-damage signal-processed 8th mode shape. (c) Difference between (a) and (b).
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Case study – continued
The CWT plotted in Fig. c in the previous slide is converted to a simple statistical damage indicator. States 1-4 are damaged, while states 5-8 are undamaged.
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Statistical threshold: above = no damage below = damage
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Conclusions
• Modal parameters of the lower modes are not the best indicators of a damage.
• For damage localization and especially assessment, known methods are highly dependent on the number of measurement points (e.g. number of accelerometers).
• Wavelet transformation shows potential for damage identification in wind turbine blades.
• A study on the general applicability of the method is necessary. The study includes, i.a.: – Tests with rotating blade (full operational condition).
– Measurement point density.
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Thank you for your attention.
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