Shah

Over the past two decades, more holistic SHM approaches have progressively gained interest. Such concepts were presented in section 1.3.1. In this sense, the present dissertation is based on the conception that the consideration of SHM problems should not be restricted to the observation of damage features, but should also address further aspects, such as the effect of EOCs and the approaches used for decision making. The main focus of this work lies in wind energy applications and in the exploitation of structural response data for the detection of structural changes. However, the applicability of the presented research is not limited to the field of wind energy but also expands into other fields.

Vibration-based SHM offers a plethora of approaches, which can be used for assessing the state of a structure. Modal parameters, model coefficients and model residues are some of the most widely used damage features. The majority of approaches are often based on assumptions, which are violated in real-life appli- cations and during the operation of structures. Two examples are the assumption of stationarity for vector autoregressive models and the assumption of white-noise excitation for the stochastic subspace identification approach. During the operation

of a wind turbine, for instance, both aforementioned assumptions are violated due to the rotor frequencies and the varying EOCs. The violation of these assumptions is a limiting factor, which raises doubts about the accuracy of some methods and sometimes does not allow their deployment. In sections 1.3.3 and 1.3.4, it was shown that research has been performed on system pole migration and transmissibility function models. However, up to now, no studies have been conducted on the migration of poles obtained from transmissibility functions.

It can be stated that it is unlikely that a damage feature is sensitive to all damage scenarios, since each damage feature exhibits different sensitivity to different types of damage or environmental settings. For instance, there are often discrepancies between the desicions of two damage features even on the exact same dataset. Parallel observation of different damage features can be advantageous and offer a better overview of the structural state. Yet, up to the present moment, little research has been conducted on the combination of damage feature decisions and on the exploitaton of decision making processes involved in one damage feature for improving the detection rate of another damage feature.

The first research objective of this thesis is the development of a damage feature, which does not rely on any significant assumptions. For this purpose, a modified autoregressive model with exogenous input (ARX) is considered. In the classical ARX model, external forces are used as input to the model. Due to the lack of this information for many applications, especially for an operating wind turbine, structural responses measured in one position of the structure are used as input to predict the responses in other positions. As a result, the modified ARX model becomes a transmissibility function model, whose poles do not correspond to the system’s natural frequencies and damping. Instead, they represent the zeros of the system, which constitute the positions, in which the system attenuates the input.

Structural changes affect sytem poles and zeros, which, for the case of the modified ARX model, will be called transmissibility function (TF) poles and TF zeros. Thus, structural changes cause migration of TF poles on the complex plane, on which they are presented. The monitoring of TF poles can be beneficial because they do not involve system excitation and, at the same time, encapsulate frequency and damping information. The objective of this work is to understand the mechanism behind TF pole migration and to develop a new damage feature based on it, which is appropriate for online monitoring, i.e., for monitoring in unsupervised mode.

Another research objective of this thesis lies in the combination of the decisions of different damage features. In the context of SHM, classifiers are rules, which map data to specific classes, such as "healthy" or "damaged" based on damage feature values. Boosting algorithms aim at improving the performance of learning algorithms by combining multiple base classifiers. In this work, adaptive boosting (AdaBoost) is proposed for building a strong classifier based on the classifiers of individual damage features. The objective is to combine the decisions of various damage features and to exploit them in order to build a more powerful decision rule and improve detection performance. The suggested concept can be employed following any SHM process,

which evaluates damage features and provides a decision regarding the structural state. The focus of this work lies in employing AdaBoost after the application of the three-tier SHM framework, which was presented in section 1.3.1. The modular nature of the three-tier SHM framework is ideally suited for boosting, because each connection between CPs and HT can be conceived as a classifier which constitutes a weak classifier in the boosting algorithm. Aim of this thesis is to exploit information from the SHM framework classifiers in order to compose a strong classifier which has the form of a weighted sum and is able to classify values of different CPs.

Several databases have been analyzed in order to conduct these studies. These include the experimental data of test structures and real structures, as well as data from real structures in operation. In particular, for the investigation of AdaBoost, the deployment of several damage features was required. All these analyses were carried out in the context of a holistic SHM procedure, which takes into account the variation of EOCs. A byproduct of these analyses is information on the detection performance of different damage features under consideration of EOCs variability. Therefore, a further goal of the present thesis is to perform a compara- tive study of sensitivity to damage and EOCs variability for different damage features.