A novel bi-component structural health monitoring strategy for deriving global models of operational wind turbines
Date Issued
2016-07
Author(s)
Bogoevska, S.
Spiridonakos, M.
Chatzi, E.
Dumova-Jovanoska, E.
Hoeffer, R.
Abstract
The short and long-term variability characterizing operational Wind Turbine (WT) structures
limits applicability of existing Structural Health Monitoring (SHM) strategies for diagnostics
and condition assessment. In this paper, a novel modeling approach is proposed delivering
global models able to account for a wide range of operational conditions of a WT System. The
approach relies on the merging of environmental and operational variables into the modeling
of monitored vibration response via a two-step methodology: a) implementation of a
Smoothness Priors Time Varying Autoregressive Moving Average (SP-TARMA) method for
modeling the non-stationary response, and b) implementation of a Polynomial Chaos
Expansion (PCE) probabilistic model for modeling the response uncertainty. The bicomponent
tool is applied on long-term data, collected as part of a continuous monitoring
campaign on a real operating WT structure located in Dortmund, Germany. The delivered
statistical model of the structure yields a robust representation of the underlying structural
dynamics, distinguishing actual structural damage from performance shifts attributed to
environmental and operational agents.
limits applicability of existing Structural Health Monitoring (SHM) strategies for diagnostics
and condition assessment. In this paper, a novel modeling approach is proposed delivering
global models able to account for a wide range of operational conditions of a WT System. The
approach relies on the merging of environmental and operational variables into the modeling
of monitored vibration response via a two-step methodology: a) implementation of a
Smoothness Priors Time Varying Autoregressive Moving Average (SP-TARMA) method for
modeling the non-stationary response, and b) implementation of a Polynomial Chaos
Expansion (PCE) probabilistic model for modeling the response uncertainty. The bicomponent
tool is applied on long-term data, collected as part of a continuous monitoring
campaign on a real operating WT structure located in Dortmund, Germany. The delivered
statistical model of the structure yields a robust representation of the underlying structural
dynamics, distinguishing actual structural damage from performance shifts attributed to
environmental and operational agents.
Subjects
File(s)![Thumbnail Image]()
Loading...
Name
Bogoevska et al.-EWSHM-2016.pdf
Size
748.76 KB
Format
Adobe PDF
Checksum
(MD5):24e3c6ff5e1f38cdfff764c997a9daad