A data-driven diagnostic tool for wind turbines under operational variability
Date Issued
2018-08
Author(s)
Bogoevska, S.
Chatzi, E.
Dumova-Jovanoska, E.
Höffer, R.
Abstract
The need for real-time condition assessment of complex systems relies on implementation of holistic Structural Health Monitoring (SHM) strategies that are capable of tracking structural behavior in a complete operational spectrum of the structure, distinguishing between true system changes and nonthreatening variations.
The proposed data-driven framework utilizes an autonomous bi-component tool able to link monitored structural response with random evolution of Environmental and Operational Parameters (EOP) affecting the monitored system. The approach combines the implementation of a Smoothness Priors Time Varying Autoregressive Moving Average (SP-TARMA) method for modeling the temporal variability in 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 twenty one-month tracking of the proposed PCE-SPTARMA diagnostic index, further assessed by means of statistic-based analysis, demonstrates that the proposed symbiotic treatment yields a robust model, capable of separating benign EOP fluctuations from potential pattern alterations due to actual structural damage. The obtained data-driven model verifies the future prospective of the strategy for development of an automated SHM diagnostic tool.
The proposed data-driven framework utilizes an autonomous bi-component tool able to link monitored structural response with random evolution of Environmental and Operational Parameters (EOP) affecting the monitored system. The approach combines the implementation of a Smoothness Priors Time Varying Autoregressive Moving Average (SP-TARMA) method for modeling the temporal variability in 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 twenty one-month tracking of the proposed PCE-SPTARMA diagnostic index, further assessed by means of statistic-based analysis, demonstrates that the proposed symbiotic treatment yields a robust model, capable of separating benign EOP fluctuations from potential pattern alterations due to actual structural damage. The obtained data-driven model verifies the future prospective of the strategy for development of an automated SHM diagnostic tool.
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