## 1 Introduction
Therefore, DOE principles can be used to design optimized experiments that mitigate the impact of the aforementioned sources of variance on the quality of the information obtained from investigating the responses of WT drivetrain components to different loading conditions. The goal of this investigation is to design experiments that efficiently cover the design space of the 6-degrees of freedom (6-DOF) WT rotor loads and rotational speed (herein after referred to as \"factors\") in order to assess the effect of those factors on responses of WT drivetrain components, such as deformations and misalignments. A system of sensors is utilized to measure said responses. This system is eventually intended to be used for an indirect estimation of said factors with the help of algorithms developed for the same purpose. A design space in the context of DOE is the 'n'-dimensional object containing the ranges of the set of 'n' independent variables, or factors, to be covered during experimentation [32]. To further clarify what is meant by the design space in this context, Figure 1a illustrates a design space of three factors, Torque (Mx); Thrust (Fx); and rotational speed (n), with the minimum of all three factors being zero in this example. Since the planned experiments in this investigation involve seven factors, 6-DOF rotor loads and rotational speed, the design space in the present study is 7-dimensional. In order to design multifactorial experiments for achieving the stated goal, the ranges of the factors must be defined to specify the design space. In addition, as [PERSON] suggested, the equipment available must also be capable of manipulating the factors to conduct the envisaged multifactorial experiments. Fortunately, purpose-built WT system test rigs offer a solution capable of meeting the demands of a multifactorial experiment in the context of this investigation. WT system test rigs, such as the one shown in Figure 2, allow investigators to subject a WT drivetrain to different combinations of the loads it is expected to experience during its lifetime in a controlled environment. By instrumenting the WT drivetrain components under investigation, experiments can be designed to capture the responses of interest and the behavior of the system as it experiences the applied loading conditions. Since such tests are resource-intensive, the different combinations of loads can be applied in quick succession in so-called accelerated tests [33], taking [PERSON]'s famous analogy further from a \"questionnaire\" [34] (p. 511) for nature to answer to a quick round of Q&A. Accelerated tests generally fall in three categories, which are differentiated by their respective aim: overstressing, increasing usage rates, and tightening the failure threshold [35]. The choice of category depends on the purpose of the experiment. For a more in depth discussion of accelerated tests, [PERSON] et al. provide a selection of highly relevant resources [36]: Chapter 7 of [PERSON] [35], Chapter 6 of [PERSON] [37], and Chapters 18 and 19 of [PERSON] et al. [38]. In order to design accelerated tests that efficiently cover the design space, data from targeted WT simulations are analyzed in this investigation. Using a binning process designed during this investigation to identify factor levels and combinations thereof, this paper presents a method for generating the desired test series. The method achieves this goal by combining multivariate analysis of simulated data with fundamental DOE techniques, such as randomization and replication. As can be seen in Figure 1a, an infinite number of factor combinations can be selected within the continuous design space. Therefore, factor levels are typically chosen to span the range of each respective factor with discrete levels along its range [39]. The goal is to selectively and objectively sample the design space of interest in a sparse manner [40]. Figure 1b illustrates a design space of three factors with five factor levels spanning the range of each factor.