Data-driven frameworks for robust and interpretable damage detection in wind turbine blades
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Date
29/07/2022Author
Movsessian, Artur
Metadata
Abstract
Wind energy has rapidly become one of the pillars of the energy transition generating
more than 20% of the world’s renewable electricity in 2019. By the end of 2020, 743
GW of wind capacity had been installed globally. However, to limit global average
temperature increases to 1.5°C, approx. 180 GW (including both onshore and offshore
wind) must be installed annually until 2030.
To ramp up wind energy’s contribution to the energy transition, the cost competitiveness
of this technology must be addressed. The levelised cost of electricity (LCOE)
from wind is heavily influenced by operational costs. Approx. 30% of the LCOE
from onshore wind installations corresponds to operation and maintenance (O&M)
costs. This share is higher for offshore wind installations due to the harsher marine
environment, skilled labour and specialised vessels required, among other reasons.
Maintenance, particularly unscheduled interventions, contribute to downtime. Blades
represent a critical component for the reliable operation of wind farms with a failure
rate of 0.7 occurrences per year and per turbine in the case of offshore wind. Economic
and safety consequences are anticipated when such failures are not timely identified.
Blade failures have been identified as the main cause of accidents in wind turbines
between 2012 and 2020. Therefore, the research presented in this dissertation is
motivated by the frequency and severity of failures in wind turbine blades (WTBs).
Vibration-based structural health monitoring (VSHM) systems can contribute to reducing
the number of failures and accidents, enhancing profitability, reliability and
safety of wind farms. With the rapid development of sensing technologies, VSHM
systems enable online and continuous remote integrity monitoring. VSHM systems,
which are based on the measurement of vibration signals on structures, have been
successfully implemented in offshore oil platforms, aerospace structures and bridges
where the damage location is generally unknown and/or accessibility issues arise.
Wind turbines share these challenges, yet VSHM systems have not been widely implemented.
One of the prevailing challenges for damage detection in WTBs using VSHM systems
is the influence of environmental and operational variabilities (EOVs) on the structure
and, ultimately, on the sensor readings. EOVs such as temperature, wind conditions
and moisture and humidity levels can either camouflage damage or falsely indicate
damage. Despite significant efforts to mitigate the influence of EOVs on damage
detection and the various approaches adopted by the scientific community to face
this challenge, there is no consensus on one single approach that can be generalised
and identified as the most accurate and reliable methodology for damage detection.
This dissertation introduces a robust, interpretable, data-driven and semi-supervised
damage detection framework based on machine learning techniques to address the
influence of EOVs.
Contrary to conventional damage detection frameworks, where the damage sensitive
features (DSFs) are normalised, in this dissertation’s proposed framework, the normalisation
of the EOVs is aimed at the novelty index. The effects of EOVs remain
perceptible in the novelty indices and are later mitigated through a supervised regression
model that is trained to learn the relationships between the DSFs extracted
from vibration measurements from an in-operation wind turbine blade, EOVs and
novelty indices.
The damage detection framework proposed results from the combination of two approaches:
one approach addresses the robustness of the damage detection process
and the second approach addresses the issue of interpretability of data-driven novelty
detection techniques. Different combinations of techniques including artificial neural
networks (ANN), gradient boosted decision trees (XGBoost DT) and Shapley Additive
Explanations (SHAP) were tested on data extracted from an operating Vestas
V27 wind turbine blade and compared to the widely adopted Mahalanobis distance
(MD)-based approach.
The experiments and analyses conducted throughout this dissertation have shown
that the ANN-based approach provides a mitigated novelty index that leads to high
damage detection accuracy and the interpretable ML techniques, i.e., the XGBoost
DT - SHAP sequence, facilitate the understanding of the effect of EOVs on the
monitored structure without mitigating this effect. The XGBoost - SHAP sequence
proposed results in a trade-off between accuracy and interpretability, however, this approach
still provides higher accuracy relative to the MD-based approach. Ultimately,
the choice of approach for damage detection will be application-specific and requires
domain knowledge and expertise. The operator should decide whether the trade-off
between accuracy and interpretability is acceptable for the monitored structure.