Data-driven structural health monitoring and diagnosis of operating wind turbines
Date Issued
2019-10
Author(s)
Bogoevska, S.
Chatzi, E.
Dumova-Jovanoska, E.
Höffer, R.
Abstract
The necessity for real-time condition assessment of operational full-scale systems is nowadays progressively accentuated with the decreased or indeterminate reliability of existing infrastructure, as well as the continuous utilization of new materials for design of lighter, albeit more productive structures. Structural Health Monitoring (SHM)-based diagnosis holds considerable potential for tackling the uncertainties associated to the customarily exploited simulation-based approaches, and as a result, can facilitate long-term, automated and even online assessment of in-service structures.
The growing needs and rising trends for energy savings and recycling have placed Wind Turbines (WTs) among those infrastructures that bear critical importance. Unlike conventional structures, WTs are characterized by temporal variability. We propose a diagnostic framework able to describe the variability of the monitored WT system, in this way reaching beyond the applicability margins of the traditionally utilized operational modal analysis. The novel approach combines the Smoothness Priors Time Varying Autoregressive Moving Average (SP-TARMA) method for modeling the non-stationary structural response, and a Polynomial Chaos Expansion (PCE) probabilistic model for modeling the propagation of response uncertainty.
The computational tool is applied on long-term data, collected from an active sensing system installed for four years on a real operating WT structure located in Dortmund, Germany. The long-term tracking of the obtained PCE-SPTARMA Diagnostic Indicator (DI) is further assessed by means of a statistical analysis. The results demonstrate that the proposed treatment yields a DI sensitive to unfamiliar fluctuations in measured environmental and operational parameters. The obtained data-driven model verifies the future prospective of the strategy for development of an automated SHM diagnostic tool capable of separating benign Environmental and Operational Parameters (EOP) fluctuations from pattern alterations due to actual structural damage.
The growing needs and rising trends for energy savings and recycling have placed Wind Turbines (WTs) among those infrastructures that bear critical importance. Unlike conventional structures, WTs are characterized by temporal variability. We propose a diagnostic framework able to describe the variability of the monitored WT system, in this way reaching beyond the applicability margins of the traditionally utilized operational modal analysis. The novel approach combines the Smoothness Priors Time Varying Autoregressive Moving Average (SP-TARMA) method for modeling the non-stationary structural response, and a Polynomial Chaos Expansion (PCE) probabilistic model for modeling the propagation of response uncertainty.
The computational tool is applied on long-term data, collected from an active sensing system installed for four years on a real operating WT structure located in Dortmund, Germany. The long-term tracking of the obtained PCE-SPTARMA Diagnostic Indicator (DI) is further assessed by means of a statistical analysis. The results demonstrate that the proposed treatment yields a DI sensitive to unfamiliar fluctuations in measured environmental and operational parameters. The obtained data-driven model verifies the future prospective of the strategy for development of an automated SHM diagnostic tool capable of separating benign Environmental and Operational Parameters (EOP) fluctuations from pattern alterations due to actual structural damage.
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