Damage Identification in Wind Turbine Blades
2nd Annual Blade Inspection, Damage and Repair Forum, 2014
Martin Dalgaard Ulriksen
Research Assistant, Aalborg University, Denmark
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
2
Research motivation
Reliable damage identification enables, i.a., the turbine operators to:
• optimize maintenance
• shut down in case of an emergency
3
Research motivation - continued
Cracks Edge damages Surface and coating damages
4
Cracks and edge
debondings are most critical damage types - require structural repairs.
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:
5
Lvl. 2 Lvl. 3
Lvl. 2
Basic principles of damage identification – cont.
Quantities typically used for damage identification:
• Temperature
• Noise
• Vibration
6
Basic principles of damage identification – cont.
7
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
Basic principles of damage identification – cont.
Noise-based (acoustic emission)
Basic idea: monitor the acoustic emission generated by onset or growth of damage.
8
• Advantages:
• Identifying damage areas plus hot spots and weak points
• Disadvantages:
• Relatively high acoustic energy
attenuation (diversity of materials)
Basic principles of damage identification – cont.
Vibration-based
Basic idea: monitor the vibrations and examine signal anomalies.
9
• Advantages:
• Independent of structural material
• Disadvantages:
• Sensitivity difference in modal parameters for different damage types
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
10
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)
11
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
12
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.
13
Case study
Damage identification in SSP 34 m wind turbine blade.
14
Case study – continued
Measurements during approximately seven minutes, corresponding to at least 500 oscillations at the lowest frequency of interest (≈ 1.3 Hz).
15
Only one cable for 1. Data
2. Synchronization 3. Power supply
Short accelerometer cable
Tri-axial accelero- meter mounted on swivel base
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.
16
Case study – continued
Excited by hits with foam-wrapped wooden sticks at several locations along the blade (simulating ambient vibrations).
17
Case study – continued
18
OMA setup:
• Unmeasured input: hits with foam-wrapped wooden sticks.
• Measured output: accelerations in 20 points.
1.2 m debonding
Case study – continued
Eigenfrequency findings:
19
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!
Case study – continued
Mode shape findings:
• No traces of the damage at the lowest modes
20
1st flapwise mode 1st edgewise mode
Case study – continued
Mode shape findings:
• No traces of the damage at the lowest modes
• Some differences occur in the higher modes
21
8th mode (combination of flap and edge)
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.
22
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).
23
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.
24
Statistical threshold:
above = no damage below = damage
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.
25
Thank you for your attention.
26