Details
| Original language | English |
|---|---|
| Title of host publication | 8th European Workshop on Structural Health Monitoring, EWSHM 2016 |
| Pages | 2351-2360 |
| Number of pages | 10 |
| ISBN (electronic) | 9781510827936 |
| Publication status | Published - 2016 |
| Event | 8th European Workshop on Structural Health Monitoring, EWSHM 2016 - Bilbao, Spain Duration: 5 Jul 2016 → 8 Jul 2016 |
Publication series
| Name | 8th European Workshop on Structural Health Monitoring, EWSHM 2016 |
|---|---|
| Volume | 3 |
Abstract
In the current work, a vibration-based SHM-scheme and an acoustic emission (AE) approach based on airborne sound are tested for damage detection at wind turbine rotor blades. The vibration-based approach includes the estimation of condition parameters (CPs), machine learning by means of data classification for changing environmental and operational conditions (EOCs) and hypothesis testing by using the acceleration signals of six measurement positions that are distributed over the blade length. A residue from the stochastic subspace identification (SSI) method and a residue from a vector autoregressive (VAR) model were used, in order to obtain two CPs. These are used as indicators for changes in the response of the structure. The airborne sound acoustic mission damage detection approach monitors the blade with three fiber optical microphones. A model of the cracking sound was developed, which describes characteristics of these sounds in the time-frequencypower domain. A detection algorithm uses these characteristics to detect damages, to estimate their significance and to handle environmental noise. Both methods were applied on data from a fatigue test of a 34 m rotor blade, which was harmonically excited for over one million load cycles in edgewise direction, leading to a significant damage at the trailing edge. Further, the potential of combining the two complementary approaches is investigated.
Keywords
- Acoustic emission, Fatigue test, Rotor blades, SHM-scheme, Vibration-based, Wind turbine
ASJC Scopus subject areas
- Health Professions(all)
- Health Information Management
- Computer Science(all)
- Computer Science Applications
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8th European Workshop on Structural Health Monitoring, EWSHM 2016. 2016. p. 2351-2360 (8th European Workshop on Structural Health Monitoring, EWSHM 2016; Vol. 3).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Combining a vibration-based SHM-scheme and an airborne sound approach for damage detection on wind turbine rotor blades
AU - Tsiapoki, Stavroula
AU - Krause, Thomas
AU - Häckell, Moritz W.
AU - Rolfes, Raimund
AU - Ostermann, Jörn
PY - 2016
Y1 - 2016
N2 - In the current work, a vibration-based SHM-scheme and an acoustic emission (AE) approach based on airborne sound are tested for damage detection at wind turbine rotor blades. The vibration-based approach includes the estimation of condition parameters (CPs), machine learning by means of data classification for changing environmental and operational conditions (EOCs) and hypothesis testing by using the acceleration signals of six measurement positions that are distributed over the blade length. A residue from the stochastic subspace identification (SSI) method and a residue from a vector autoregressive (VAR) model were used, in order to obtain two CPs. These are used as indicators for changes in the response of the structure. The airborne sound acoustic mission damage detection approach monitors the blade with three fiber optical microphones. A model of the cracking sound was developed, which describes characteristics of these sounds in the time-frequencypower domain. A detection algorithm uses these characteristics to detect damages, to estimate their significance and to handle environmental noise. Both methods were applied on data from a fatigue test of a 34 m rotor blade, which was harmonically excited for over one million load cycles in edgewise direction, leading to a significant damage at the trailing edge. Further, the potential of combining the two complementary approaches is investigated.
AB - In the current work, a vibration-based SHM-scheme and an acoustic emission (AE) approach based on airborne sound are tested for damage detection at wind turbine rotor blades. The vibration-based approach includes the estimation of condition parameters (CPs), machine learning by means of data classification for changing environmental and operational conditions (EOCs) and hypothesis testing by using the acceleration signals of six measurement positions that are distributed over the blade length. A residue from the stochastic subspace identification (SSI) method and a residue from a vector autoregressive (VAR) model were used, in order to obtain two CPs. These are used as indicators for changes in the response of the structure. The airborne sound acoustic mission damage detection approach monitors the blade with three fiber optical microphones. A model of the cracking sound was developed, which describes characteristics of these sounds in the time-frequencypower domain. A detection algorithm uses these characteristics to detect damages, to estimate their significance and to handle environmental noise. Both methods were applied on data from a fatigue test of a 34 m rotor blade, which was harmonically excited for over one million load cycles in edgewise direction, leading to a significant damage at the trailing edge. Further, the potential of combining the two complementary approaches is investigated.
KW - Acoustic emission
KW - Fatigue test
KW - Rotor blades
KW - SHM-scheme
KW - Vibration-based
KW - Wind turbine
UR - http://www.scopus.com/inward/record.url?scp=84995528742&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84995528742
T3 - 8th European Workshop on Structural Health Monitoring, EWSHM 2016
SP - 2351
EP - 2360
BT - 8th European Workshop on Structural Health Monitoring, EWSHM 2016
T2 - 8th European Workshop on Structural Health Monitoring, EWSHM 2016
Y2 - 5 July 2016 through 8 July 2016
ER -