Damage Identification in Wind Turbine Blades

Transcription

1 Damage Identification in Wind Turbine Blades 2 nd Annual Blade Inspection, Damage and Repair Forum, 2014 Martin Dalgaard Ulriksen Research Assistant, Aalborg University, Denmark

2 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

3 Research motivation Reliable damage identification enables, i.a., the turbine operators to: optimize maintenance shut down in case of an emergency 3

4 Research motivation - continued Cracks Edge damages Surface and coating damages Cracks and edge debondings are most critical damage types - require structural repairs. 4

5 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: Lvl. 2 Lvl. 3 5

6 Basic principles of damage identification cont. Quantities typically used for damage identification: Temperature Noise Vibration 6

7 Basic principles of damage identification cont. 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 7

8 Basic principles of damage identification cont. Noise-based (acoustic emission) Basic idea: monitor the acoustic emission generated by onset or growth of damage. Advantages: Identifying damage areas plus hot spots and weak points Disadvantages: Relatively high acoustic energy attenuation (diversity of materials) 8

9 Basic principles of damage identification cont. Vibration-based Basic idea: monitor the vibrations and examine signal anomalies. Advantages: Independent of structural material Disadvantages: Sensitivity difference in modal parameters for different damage types 9

10 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

11 Vibration-based damage identification Vibrations can be measured as, e.g., displacements, velocities, and accelerations. Common for wind turbines is to mount wireless 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

12 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

13 Vibration-based damage identification cont. Numerous damage identification methods utilizing eigenfrequencies 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

14 Case study Damage identification in SSP 34 m wind turbine blade. 14

15 Case study continued Measurements during approximately seven minutes, corresponding to at least 500 oscillations at the lowest frequency of interest ( 1.3 Hz). Only one cable for 1. Data 2. Synchronization 3. Power supply Short accelerometer cable Tri-axial accelerometer mounted on swivel base 15

16 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

17 Case study continued Excited by hits with foam-wrapped wooden sticks at several locations along the blade (simulating ambient vibrations). 17

18 Case study continued OMA setup: Unmeasured input: hits with foam-wrapped wooden sticks. Measured output: accelerations in 20 points. 1.2 m debonding 18

19 Case study continued Eigenfrequency findings: Natural frequencies, Hz Undamaged Damaged Diff.,% Mode Name Mean Confid.,% Mean Confid.,% 1 1st flap % % 0.48% 2 1st edge % % -0.10% 3 2nd flap % % 0.09% 4 2nd edge % % 0.11% 5 3rd flap % % 0.18% 6 1st torsion % % 0.24% 7 4th flap + 3rd edge % % 0.10% The difference is much smaller than the confidence! 19

20 Case study continued Mode shape findings: No traces of the damage at the lowest modes 1 st flapwise mode 1 st edgewise mode 20

21 Case study continued Mode shape findings: No traces of the damage at the lowest modes Some differences occur in the higher modes 8 th mode (combination of flap and edge) 21

22 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

23 Case study continued CWT results by use of 8 th mode (combination of 3 rd edgewise and 4 th flapwise bending modes) and a 4 th order Gaussian wavelet: (a) CWT of post-damage signal-processed 8 th mode shape. (b) CWT of pre-damage signal-processed 8 th mode shape. (c) Difference between (a) and (b). 23

24 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. Statistical threshold: above = no damage below = damage 24

25 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

26 Thank you for your attention. 26