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

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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.

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

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

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)

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

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

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

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!

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

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)

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

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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