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.

This paper presents a novel model order reduction technique for 3D flexible multibody systems featuring nonlinear elastic behavior. We adopt the mean-axis floating frame approach in combination with an enhanced Rubin substructuring technique for the construction of the reduction basis. The standard Rubin reduction basis is augmented with the modal derivatives of both free-interface vibration modes and attachment modes to consider the bending–stretching coupling effects for each flexible body. The mean-axis frame generally yields relative displacements and rotations of smaller magnitude when compared to the one obtained by the nodal-fixed floating frame. This positively impacts the accuracy of the reduction basis. Also, when equipped with modal derivatives, the Rubin method better considers the geometric nonlinearities than the Craig–Bampton method, as it comprises vibration modes and modal derivatives featuring free motion of the interface. The nonlinear coupling between free-interface modes and attachment modes is also considered. Numerical tests confirm that the proposed method is more accurate than Craig–Bampton’s, a nodal fixed floating frame counterpart originally proposed in Wu and Tiso (Multibody Syst. Dyn. 36(4): 405–425, [ 2016 ]), and produces significant speed-ups. However, the offline cost is increased because the mean-axis formulation produces operators with decreased sparsity patterns.

Structural health monitoring (SHM) has been increasingly exploited in recent years as a valuable tool for assessing performance throughout the life cycle of structural systems, as well as for supporting decision-making and maintenance planning. Although a great assortment of SHM methods has been developed, only a limited number of studies exist serving as reference basis for the comparison of different techniques. In this paper, the vibration-based assessment of a small-scale wind turbine (WT) blade is experimentally investigated, with the aim of establishing a benchmark case study for the SHM community. The structure under consideration, provided by Sonkyo Energy as part of the Windspot 3.5 kW WT model, is tested in both healthy and damaged states under varying environmental, that is, temperature, conditions as imposed by means of a climatic chamber. This study offers a thorough documentation of the configuration of this experimental benchmark, including the types of deployed sensors, the nature of excitation and available measurements, and the investigated damage scenarios and environmental variations enforced. Lastly, an overview of the raw and processed measurement data, made available to researchers via an open access Zenodo repository, is herein provided.

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