Details
| Original language | English |
|---|---|
| Pages | 796-803 |
| Number of pages | 8 |
| Publication status | Published - 2014 |
| Event | 7th European Workshop on Structural Health Monitoring, EWSHM 2014 - Nantes, France Duration: 8 Jul 2014 → 11 Jul 2014 |
Conference
| Conference | 7th European Workshop on Structural Health Monitoring, EWSHM 2014 |
|---|---|
| Country/Territory | France |
| City | Nantes |
| Period | 8 Jul 2014 → 11 Jul 2014 |
Abstract
Many countries worldwide and in Europe still have the goal of a future cut of CO2 emission in common. A shift from fossil to renewable energy source is the logical consequence. (Offshore) wind turbines ((O)WTs) play an important role in the so called "green" energy sector. An increasing number of remote offshore plants and an ageing fleet of onshore structures raise the demand of structural health monitoring (SHM) in this field. Guidelines still lack firm establishments and SHM is supposed to help assuring a safe operation and a possible extension of the lifetime. The work presented displays a modular SHM scheme applicable for engineering structures under varying environmental and operational conditions (EOCs). The procedure is applied to a 5MW OWT in the German bight, located in the test field alpha ventus. The integration into and application of the complete SHM scheme is presented through different condition parameters (CPs), machine learning (data classification) and hypothesis testing.
Keywords
- Affinity propagation, Condition parameter, Control charts, Machine learning, Offshore wind turbine
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Engineering(all)
- Building and Construction
- Computer Science(all)
- Computer Science Applications
Sustainable Development Goals
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2014. 796-803 Paper presented at 7th European Workshop on Structural Health Monitoring, EWSHM 2014, Nantes, France.
Research output: Contribution to conference › Paper › Research › peer review
}
TY - CONF
T1 - A Modular SHM-Scheme for Engineering Structures under Changing Conditions
T2 - 7th European Workshop on Structural Health Monitoring, EWSHM 2014
AU - Häckell, Moritz W.
AU - Rolfes, Raimund
PY - 2014
Y1 - 2014
N2 - Many countries worldwide and in Europe still have the goal of a future cut of CO2 emission in common. A shift from fossil to renewable energy source is the logical consequence. (Offshore) wind turbines ((O)WTs) play an important role in the so called "green" energy sector. An increasing number of remote offshore plants and an ageing fleet of onshore structures raise the demand of structural health monitoring (SHM) in this field. Guidelines still lack firm establishments and SHM is supposed to help assuring a safe operation and a possible extension of the lifetime. The work presented displays a modular SHM scheme applicable for engineering structures under varying environmental and operational conditions (EOCs). The procedure is applied to a 5MW OWT in the German bight, located in the test field alpha ventus. The integration into and application of the complete SHM scheme is presented through different condition parameters (CPs), machine learning (data classification) and hypothesis testing.
AB - Many countries worldwide and in Europe still have the goal of a future cut of CO2 emission in common. A shift from fossil to renewable energy source is the logical consequence. (Offshore) wind turbines ((O)WTs) play an important role in the so called "green" energy sector. An increasing number of remote offshore plants and an ageing fleet of onshore structures raise the demand of structural health monitoring (SHM) in this field. Guidelines still lack firm establishments and SHM is supposed to help assuring a safe operation and a possible extension of the lifetime. The work presented displays a modular SHM scheme applicable for engineering structures under varying environmental and operational conditions (EOCs). The procedure is applied to a 5MW OWT in the German bight, located in the test field alpha ventus. The integration into and application of the complete SHM scheme is presented through different condition parameters (CPs), machine learning (data classification) and hypothesis testing.
KW - Affinity propagation
KW - Condition parameter
KW - Control charts
KW - Machine learning
KW - Offshore wind turbine
UR - http://www.scopus.com/inward/record.url?scp=84939428601&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:84939428601
SP - 796
EP - 803
Y2 - 8 July 2014 through 11 July 2014
ER -