## 1 Introduction
### State of the Art  
Various methods exist for detecting laminar-turbulent flow transitions in thermograms, yet most approaches explicitly ignore turbulence wedges. One such approach called 'local infrared thermography' is presented by [PERSON] et al. [10] to detect unsteady transitions in periodic pitching processes. A pitching airfoil was investigated in a wind tunnel and thermograms were captured over multiple pitching periods. The airfoil was externally heated with a spotlight to increase the temperature difference between the airfoil and boundary layer flow. To detect the flow transition, the intensities of each pixel were detected over time and assigned to the corresponding phase of the pitch angle. Extrema in the intensity signal mean that the transition is passing through the current location. While the method successfully detects the flow transition, the analysis is not applicable to single thermograms, which is often the only type of data available from field measurements of in-service wind turbines. As mentioned before, the external active heating is also not often realized in field measurements. [PERSON] et al. [11] located the flow transition in a crossflow-dominated environment on a heated swept wing model. The wing model was tested in wind tunnel and flight experiments, where the model was mounted on an airplane. The crossflow-dominated environment produces a jagged transition front, also called a sawtooth transition pattern. This sawtooth pattern is similar to turbulence wedges, albeit on a smaller scale. Thus, the measurement task is similar to detecting turbulence wedges. Furthermore, one transition front did in fact include one turbulence wedge. The heating of the model was realized with internal electrical heating wires. A special coating which reduced reflections, combined with the heating, improved the contrast-to-noise ratio (CNR) between laminar and turbulent flow regions in the thermograms. The saw-toothed transitions were detected through local maxima in the intensity gradient profiles after a series of image filters. Afterwards, a statistical analysis was performed to determine a prominent transition position of the sawtooth transition pattern. As the focus was put on estimating a transition position from the sawtooth pattern, turbulence wedges were intentionally rejected by the analysis. This analysis works with single thermograms but utilizes many filters and processing steps to reach its results, which have to be adjusted and optimized. Furthermore, the heating of the blade and the coating of the blade cannot be realized in field measurements of wind turbines in motion. To detect the natural flow transition onset and end on helicopter rotor blades, [PERSON] and [PERSON] [12] analyzed thermograms of rotating blades. They investigated a model blade on a whirl tower as well as full-scale rotor blades on helicopters during ground run and hovering. To increase the temperature differences between the blade surface and fluid, the blade was either spun fast to cool it, cooled with ice or passively heated in sunlight. In the thermograms, intensity profiles in the direction of the chord of the blade were examined, omitting all chord-wise positions of premature transitions. The intensity profiles consist of linear sections with different slopes. The three distinct slopes can be attributed to the laminar, transitional and turbulent regions of the boundary layer flow. Each slope was estimated with a linear function. The intersection point of the linear estimate of two adjacent regions was then classified as the transition onset and transition endpoint, respectively. The natural transition was successfully detected even for low signal-to-noiseratios, which resulted from short exposure times. However, as turbulence wedges were excluded from the analysis, premature flow transitions were not detected. [PERSON] et al. [13] investigated thermograms of wind turbines in operation to determine the flow transition.