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In the context of optimized Operation & Maintenance of wind energy infrastructure, it is important to develop decision support tools, able to guide operators and engineers in the management of these assets. This task is particularly challenging given the multiplicity of uncertainties involved, from the point of view of the aggregated data, the available knowledge with respect to the wind turbine structures, and sub-components, as well as the constantly varying operational and environmental loads. We here propose to propagate wind turbine telemetry through a decision tree learning algorithm to detect faults, errors, damage, patterns, anomalies and abnormal operation. The use of decision trees is motivated by the fact that they tend to be easier to implement and interpret than other quantitative data-driven methods. Furthermore, the telemetry consists of data from condition and structural health monitoring systems, which lends itself nicely in the field of Big Data as large amounts are continuously sampled at high rate from thousands of wind turbines. In this paper, we review several decision tree algorithms formerly proposed by the machine learning community (i.e. ID3, C4.5, C5.0, J48, SPRINT, FACT, FIRM, SLIQ, CHAID, QUEST, CRUISE, PUBLIC,BOAT, RAINFOREST, MARS, RIPPER and CART), we then train an ensemble Bagged decision tree classifier on a large condition monitoring dataset from an offshore wind farm comprising 48 wind turbines, and use it to automatically extract paths linking excessive vibrations faults to their possible root causes. We finally give an outlook of an architecture to implement decision tree learning in the context of cloud computing for big data, namely involving a cloud based Apache Hadoop software for very large data storage and handling, and Apache Spark for efficiently running machine-learning algorithms.

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In this work, a computational framework is proposed for fatigue damage estimation in structural systems by integrating operational experimental measurements in a high-fidelity, large-scale finite element model. The proposed method is applied in a linear steel substructure of a lignite grinder assembly at a Public Power Corporation power plant. A finite element model of the steel base is developed and updated to match the dynamic characteristics measured in real operating conditions. This is achieved through coupled use of numerical and experimental methods for identifying, updating, and optimizing a high-fidelity finite element model. The full stress time histories of the complex mechanical assembly are estimated, at critical locations, by imposing operational vibration measurements from a limited number of sensors in the updated finite element model. Fatigue damage and remaining lifetime is subsequently estimated via commonly adopted engineering approaches, such as Palmgren-Miner damage rule, S-N curves, and rainflow cycle counting. Incorporation of a numerical model of the structure in the response estimation procedure permits stress estimation at unmeasured locations, thereby enabling the drawing of a complete and substantially dense fatigue map consistent with the vibration measurements. Fatigue predictions via the proposed framework are highly correlated to experimental fatigue results, proving the efficiency and applicability of the framework.

Structural health monitoring offers an attractive tool for condition assessment, fault prognosis and life-cycle management of wind turbine components. However, owing to the intense loading conditions, geometrical nonlinearities, complex material properties and the lack of real-time information on induced structural response, damage detection and characterization of structural components comprise a challenging task. This study is focused on the problem of damage detection for a small-scale wind turbine (Sonkyo Energy Windspot 3.5 kW) experimental blade. To this end, the blade is dynamically tested in both its nominal (healthy) condition and for artificially induced damage of varying types and intensities. The response is monitored via a set of accelerometers; the acquired signals serve for damage detection via the use of appropriate statistical and modal damage detection methods. The former rely on extraction of a characteristic statistical quantity and establishment of an associated statistical hypothesis test, while the latter rely on tracking of damage-sensitive variations of modal properties. The results indicate that statistical-based methods outperform modal-based ones, succeeding in the detection of induced damage, even at low levels.

We investigate the interaction of guided surface acoustic modes (GSAMs) in unconsolidated granular media with a metasurface, consisting of an array of vertical oscillators. We experimentally observe the hybridization of the lowest-order GSAM at the metasurface resonance, and note the absence of mode delocalization found in homogeneous media. Our numerical studies reveal how the stiffness gradient induced by gravity in granular media causes a down-conversion of all the higher-order GSAMs, which preserves the acoustic energy confinement. We anticipate these findings to have implications in the design of seismic-wave protection devices in stratified soils.

SummaryA novel damage localization method is introduced in this study, which exploits mode shape curvatures as damage features, while accounting for operational variability. The developed framework operates in an output-only regime,that is, it does not assume availability of records from the influencing environmental/operational quantities but rather from response quantities alone. The introduced tool comprises 3 stages pertaining to training, validation, and diagnostics. During the training stage, a representation of the healthy, or baseline, structural state is acquired over varying operational conditions. A data matrix is formulated, whose individual columns correspond to mode shape curvatures at distinct operational conditions, and principal component analysis (PCA) is applied for extraction of the imprints of separate operational sources on these curvatures. To this end, a residual matrix between the original and the PCA mapped data is formed serving for statistical characterization of each mode.Subsequently, during the validation and diagnostics stages, the mode shape curvature matrices for the currently inspected structural state are assembled and the same PCA mapping is enforced. A typical hypothesis test and a corresponding damage index are then adopted in order to firstly detect damage, and to secondly localize damage, should this exist. The implementation of the proposed method in 2 numerical case studies confirms its effectiveness and the encouraging results suggest further investigation on operating structural systems.

The coupling of magnetorheological (MR) dampers with semi-active control schemes has proven to be an effective and failsafe approach for vibration mitigation of low-damped structures. However, due to the nonlinearities inherently relating to such damping devices, the characterization of the associated nonlinear phenomena is still a challenging task. Herein, an enhanced phenomenological modeling approach is proposed for the description of a rotationaltype MR damper, which comprises a modified Bouc-Wen model coupled with an appropriately selected sigmoid function. In a first step, parameter optimization is performed on the basis of individual models in an effort to approximate the experimentally observed response for varying current levels and actuator force characteristics. In a second step, based on the previously identified parameters, a generalized best-fit model is proposed by performing a regression analysis. Finally, model validation is carried out via implementation on different sets of experimental data. The proposed model indeed renders an improved representation of the actually observed nonlinear behavior of the tested rotational MR damper.