Resumé

This paper presents a case study demonstrating how to quantify the
value of structural health monitoring (SHM), when used optimally in
maintenance planning for wind turbine blades. Maintenance cost
optimization is performed using a risk-based approach based on
Bayesian decision analysis, in which probabilistic models are developed
for blade deterioration processes, blade inspections and SHM systems.
The probabilistic SHM system model is based on data from an SHM
campaign with a 225 kW Vestas V27 wind turbine, where an artificial
trailing edge crack of increasing size was introduced. The statistics
derived from this model are applied to the case study concerning
maintenance of an 8 MW offshore wind turbine. It is found that the
benefit of the SHM highly depends on the reliability of the SHM system
and on how SHM observations are used when making decisions on
inspections and maintenance. A sensitivity study confirms the generality
of the findings.
OriginalsprogEngelsk
TidsskriftStructural Health Monitoring
ISSN1475-9217
StatusAfsendt - 2019

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Structural health monitoring
Wind turbines
Turbomachine blades
Maintenance
Health
Offshore wind turbines
Decision Support Techniques
Decision theory
Statistical Models
Deterioration
Decision Making
Inspection
Decision making
Cracks
Planning

Citer dette

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title = "Risk-based maintenance of wind turbine blades with structural health monitoring",
abstract = "This paper presents a case study demonstrating how to quantify thevalue of structural health monitoring (SHM), when used optimally inmaintenance planning for wind turbine blades. Maintenance costoptimization is performed using a risk-based approach based onBayesian decision analysis, in which probabilistic models are developedfor blade deterioration processes, blade inspections and SHM systems.The probabilistic SHM system model is based on data from an SHMcampaign with a 225 kW Vestas V27 wind turbine, where an artificialtrailing edge crack of increasing size was introduced. The statisticsderived from this model are applied to the case study concerningmaintenance of an 8 MW offshore wind turbine. It is found that thebenefit of the SHM highly depends on the reliability of the SHM systemand on how SHM observations are used when making decisions oninspections and maintenance. A sensitivity study confirms the generalityof the findings.",
author = "Nielsen, {Jannie S{\o}nderk{\ae}r} and Dmitri Tcherniak and Ulriksen, {Martin Dalgaard}",
year = "2019",
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Risk-based maintenance of wind turbine blades with structural health monitoring. / Nielsen, Jannie Sønderkær; Tcherniak, Dmitri; Ulriksen, Martin Dalgaard.

I: Structural Health Monitoring, 2019.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Risk-based maintenance of wind turbine blades with structural health monitoring

AU - Nielsen, Jannie Sønderkær

AU - Tcherniak, Dmitri

AU - Ulriksen, Martin Dalgaard

PY - 2019

Y1 - 2019

N2 - This paper presents a case study demonstrating how to quantify thevalue of structural health monitoring (SHM), when used optimally inmaintenance planning for wind turbine blades. Maintenance costoptimization is performed using a risk-based approach based onBayesian decision analysis, in which probabilistic models are developedfor blade deterioration processes, blade inspections and SHM systems.The probabilistic SHM system model is based on data from an SHMcampaign with a 225 kW Vestas V27 wind turbine, where an artificialtrailing edge crack of increasing size was introduced. The statisticsderived from this model are applied to the case study concerningmaintenance of an 8 MW offshore wind turbine. It is found that thebenefit of the SHM highly depends on the reliability of the SHM systemand on how SHM observations are used when making decisions oninspections and maintenance. A sensitivity study confirms the generalityof the findings.

AB - This paper presents a case study demonstrating how to quantify thevalue of structural health monitoring (SHM), when used optimally inmaintenance planning for wind turbine blades. Maintenance costoptimization is performed using a risk-based approach based onBayesian decision analysis, in which probabilistic models are developedfor blade deterioration processes, blade inspections and SHM systems.The probabilistic SHM system model is based on data from an SHMcampaign with a 225 kW Vestas V27 wind turbine, where an artificialtrailing edge crack of increasing size was introduced. The statisticsderived from this model are applied to the case study concerningmaintenance of an 8 MW offshore wind turbine. It is found that thebenefit of the SHM highly depends on the reliability of the SHM systemand on how SHM observations are used when making decisions oninspections and maintenance. A sensitivity study confirms the generalityof the findings.

M3 - Journal article

JO - Structural Health Monitoring

JF - Structural Health Monitoring

SN - 1475-9217

ER -