Details
| Original language | English |
|---|---|
| Pages (from-to) | 1289-1312 |
| Number of pages | 24 |
| Journal | Structural health monitoring |
| Volume | 17 |
| Issue number | 5 |
| Publication status | Published - 11 Oct 2017 |
Abstract
The increasing number of installed wind turbines has led to a greater need for monitoring of their subcomponents. In particular, damages on rotor blades should be detected as early as possible, since they can cause long and hence expensive standstill times. In this work, a three-tier structural health monitoring framework is employed on the experimental data of a 34-m rotor blade for damage and ice detection. The structural health monitoring framework includes the functions of data normalization by clustering according to environmental and operational conditions, feature extraction, and hypothesis testing. In order to assess the framework and the methods applied with respect to ice detection, an ice accretion test was performed by gradually adding masses at the blade tip. First, a modal test by means of manual and impulse excitation was performed on the healthy blade and for all steps of the ice test. Subsequently, to induce damage, the blade was cyclically excited in edgewise direction for over 1 million cycles until failure occurred at the trailing edge. Finally, the initial modal test was repeated on the damaged blade. Modal parameters from system identification and further damage features, also called condition parameters, are presented and compared to each other. Results from the modal test show that structural changes due to damage at the trailing edge and added mass can be detected by changes in the condition parameters. Nevertheless, it is shown that some condition parameters exhibit higher sensitivity to damage than natural frequencies. Furthermore, a correlation between the amount of added mass and the changes in natural frequencies and some of the condition parameters is shown. For the analysis of the fatigue test, condition parameters were determined with and without prior data clustering according to the applied damage equivalent load, resulting in two realizations of the structural health monitoring framework. Results from the fatigue test show that the majority of condition parameters have good detection performance regarding structural change due to fatigue cracks and due to damage at the trailing edge for various confidence intervals. Finally, it is shown that the detection performance in the case of data clustering according to applied damage equivalent load is higher than without data clustering. This emphasizes the need of data normalization by clustering according to the environmental and operational conditions.
Keywords
- damage detection, ice detection, machine learning, rotor blades, vibration based, Wind turbines
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Biophysics
- Engineering(all)
- Mechanical Engineering
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In: Structural health monitoring, Vol. 17, No. 5, 11.10.2017, p. 1289-1312.
Research output: Contribution to journal › Article › Research
}
TY - JOUR
T1 - Damage and ice detection on wind turbine rotor blades using a three-tier modular structural health monitoring framework
AU - Tsiapoki, Stavroula
AU - Häckell, Moritz W.
AU - Grießmann, Tanja
AU - Rolfes, Raimund
N1 - Funding information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) under grant number 0325388 (SHM.Rotorblatt). The authors would like to thank Heiko Rosemann, Falko B?rkner, Gregor Basters, and Alexandros Antoniou from the Institute for Wind Energy and Energy Systems Technology (IWES, Bremerhaven) for their support and supervision during the experiment. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) under grant number 0325388 (SHM.Rotorblatt).
PY - 2017/10/11
Y1 - 2017/10/11
N2 - The increasing number of installed wind turbines has led to a greater need for monitoring of their subcomponents. In particular, damages on rotor blades should be detected as early as possible, since they can cause long and hence expensive standstill times. In this work, a three-tier structural health monitoring framework is employed on the experimental data of a 34-m rotor blade for damage and ice detection. The structural health monitoring framework includes the functions of data normalization by clustering according to environmental and operational conditions, feature extraction, and hypothesis testing. In order to assess the framework and the methods applied with respect to ice detection, an ice accretion test was performed by gradually adding masses at the blade tip. First, a modal test by means of manual and impulse excitation was performed on the healthy blade and for all steps of the ice test. Subsequently, to induce damage, the blade was cyclically excited in edgewise direction for over 1 million cycles until failure occurred at the trailing edge. Finally, the initial modal test was repeated on the damaged blade. Modal parameters from system identification and further damage features, also called condition parameters, are presented and compared to each other. Results from the modal test show that structural changes due to damage at the trailing edge and added mass can be detected by changes in the condition parameters. Nevertheless, it is shown that some condition parameters exhibit higher sensitivity to damage than natural frequencies. Furthermore, a correlation between the amount of added mass and the changes in natural frequencies and some of the condition parameters is shown. For the analysis of the fatigue test, condition parameters were determined with and without prior data clustering according to the applied damage equivalent load, resulting in two realizations of the structural health monitoring framework. Results from the fatigue test show that the majority of condition parameters have good detection performance regarding structural change due to fatigue cracks and due to damage at the trailing edge for various confidence intervals. Finally, it is shown that the detection performance in the case of data clustering according to applied damage equivalent load is higher than without data clustering. This emphasizes the need of data normalization by clustering according to the environmental and operational conditions.
AB - The increasing number of installed wind turbines has led to a greater need for monitoring of their subcomponents. In particular, damages on rotor blades should be detected as early as possible, since they can cause long and hence expensive standstill times. In this work, a three-tier structural health monitoring framework is employed on the experimental data of a 34-m rotor blade for damage and ice detection. The structural health monitoring framework includes the functions of data normalization by clustering according to environmental and operational conditions, feature extraction, and hypothesis testing. In order to assess the framework and the methods applied with respect to ice detection, an ice accretion test was performed by gradually adding masses at the blade tip. First, a modal test by means of manual and impulse excitation was performed on the healthy blade and for all steps of the ice test. Subsequently, to induce damage, the blade was cyclically excited in edgewise direction for over 1 million cycles until failure occurred at the trailing edge. Finally, the initial modal test was repeated on the damaged blade. Modal parameters from system identification and further damage features, also called condition parameters, are presented and compared to each other. Results from the modal test show that structural changes due to damage at the trailing edge and added mass can be detected by changes in the condition parameters. Nevertheless, it is shown that some condition parameters exhibit higher sensitivity to damage than natural frequencies. Furthermore, a correlation between the amount of added mass and the changes in natural frequencies and some of the condition parameters is shown. For the analysis of the fatigue test, condition parameters were determined with and without prior data clustering according to the applied damage equivalent load, resulting in two realizations of the structural health monitoring framework. Results from the fatigue test show that the majority of condition parameters have good detection performance regarding structural change due to fatigue cracks and due to damage at the trailing edge for various confidence intervals. Finally, it is shown that the detection performance in the case of data clustering according to applied damage equivalent load is higher than without data clustering. This emphasizes the need of data normalization by clustering according to the environmental and operational conditions.
KW - damage detection
KW - ice detection
KW - machine learning
KW - rotor blades
KW - vibration based
KW - Wind turbines
UR - http://www.scopus.com/inward/record.url?scp=85042079801&partnerID=8YFLogxK
U2 - 10.1177/1475921717732730
DO - 10.1177/1475921717732730
M3 - Article
AN - SCOPUS:85042079801
VL - 17
SP - 1289
EP - 1312
JO - Structural health monitoring
JF - Structural health monitoring
SN - 1475-9217
IS - 5
ER -