###### Abstract
Acoustic emission analysis (AE), based on detecting the sudden release of energy in the form of elastic waves through cracks, offers an effective way to detect and localize active sources of damage at an early stage [12]. In monitoring the machine components and rotor blades, these advantages of acoustic methods are already known and in use, particularly for detecting and localizing damage [2; 13; 14; 15; 16; 17; 18]. For the support structure, however, only a few applications are known, such as monitoring of grouted connections of offshore substructures [19]. In other engineering fields, this technology has been developed since the 20 th century to detect wire breaks in suspensions, stay cables, and pre-stressed tendons [20; 21; 22]. Thus, the motivation is to supplement or partially replace the labor-intensive visual inspections of tendons with minimally invasive methods. Therefore, non-destructive and automated methods have become the focus of attention [23]. On freely hanging cable sections, the inside of the cable can nowadays be easily and reliably inspected using self-driving robots and magnetic methods [24; 25]. However, the inspection of anchors is more difficult due to their accessibility, limited by structural enclosures. Nevertheless, these are the most heavily stressed areas due to dynamic loads and are more vulnerable to damage [26]. Ultrasound-based methods provide a possible approach for investigating these hot-spots, but usually require additional information from simulations or preliminary studies on a comparable test object before field application [23; 27]. In contrast, AE presents a compelling alternative, enabling real-time, continuous monitoring without requiring direct access to the tendons and with reduced dependence on object-specific knowledge. Unlike periodic inspections, AE operates without necessitating scheduled shutdowns. Moreover, AE is particularly effective in detecting wire break events in concealed areas, like tendons or their anchorages. Since tendon failure presents a significant risk to the structural integrity of the entire structure and can progress rapidly after initial damage, early detection is crucial for mitigating and preventing critical failure. For these reasons, the method is widely and successfully used to detect wire breaks in stay cables, suspension cables, and pre-stressed concrete bridges [28; 29; 30; 31; 32]. Acoustic emissions are caused by damage and can also arise as unwanted background noise due to friction. Such noise-related acoustic emissions must be distinguished from damage signals. During the operation of wind turbines, background noise arises from various sources and is mainly categorized as mechanical and aerodynamic noise [2; 33; 34]. Mechanical noise, which is generated by the gearbox and generator in the nacelle or by the maneuverings of the nacelle and rotor blades, is the primary noise source. It is transmitted into the tower structure as structure-borne noise. Aerodynamic noise is caused by interactions with the air, influenced by uneven loading of the blades and by their pitch angles (see Figure 1[33]). The inflow behavior and separation stall noise at the trailing edge play a role here. While trailing edge noise tends to have a broadband character, separation stall noise can be tonal in nature to the extent of a Karman vortex street. Although aerodynamic noise differs fundamentally from structure-borne noise in its origins, it is assumed that this noise is partially induced into the structural components, e.g., blade and tower, as structure-borne noise and propagates further. Accordingly, background noise sources' origins and associated correlations with generation are considered relevant to detecting wire breaks. To the authors' knowledge, the acoustic monitoring of tendons in hybrid towers is a new application. Thus, this study presents investigations on the transfer of this method from bridge engineering to wind turbines. The primary objective is to build a comprehensive database to evaluate the proposed solution.