Regular inspections of important civil infrastructures are mandatory to ensure structural safety and reliability. Until today, these inspections are primarily conducted manually, which has several deficiencies. In context of prestressed concrete structures, steel tendons can be susceptible to stress corrosion cracking, which may result in breakage of individual wires that is visually not observable. Recent research therefore suggests Acoustic Emission Monitoring for wire break detection in prestressed concrete structures. However, in noisy environments, such as wind turbines, conventional acoustic emission detection based on user-defined amplitude thresholds may not be suitable. Thus, we propose the use of matched filters for acoustic emission detection in noisy environments and apply the proposed method to the task of wire break detection in post-tensioned wind turbine towers. Based on manually conducted wire breaks and rebound hammer tests on a large-scale test frame, we employ a brute-force search for the most suitable query signal of a wire break event and a rebound hammer impact, respectively. Then, we evaluate the signal detection performance on more than 500 other wire break signals and approximately one week of continuous acoustic emission recordings in an operating wind turbine. For a signal-to-noise ratio of 0 dB, the matched filter approach shows an improvement in AUC by up to 0.78 for both, the wire break and the rebound hammer query signal, compared to state-of-the-art amplitude-based detection. Even for the unscaled wire break measurements originally recorded at the 12 m large laboratory test frame, the improvement in AUC still lies between 0.01 and 0.25 depending on the wind turbine noise recordings considered for evaluation. Matched filters may therefore be a promising alternative to amplitude-based detection algorithms and deserve particular consideration with regard to Acoustic Emission Monitoring, especially in noisy environments or when sparse senor networks are required.

Acceleration sensors are vital for assessing engineering structures by measuring properties like natural frequencies. In practice, engineering structures often have low natural frequencies and face harsh environmental conditions. Understanding sensor behavior on such structures is crucial for reliable measurements. The research focus is on understanding the behavior of acceleration sensors in harsh environmental conditions within the low-frequency acceleration range. The main question is how to distinguish sensor behavior from structural influences to minimize errors in assessing engineering structure conditions. To investigate this, the sensors are tested using a long-stroke calibration unit under varying temperature and humidity conditions. Additionally, a mini-monitoring system configured with four IEPE sensors is applied to a small-scale support structure within a climate chamber. For the evaluation, a signal-energy approach is employed to distinguish sensor behavior from structural behavior. The findings show that IEPE sensors display temperature-dependent nonlinear transmission behavior within the low-frequency acceleration range, with humidity having negligible impact. To ensure accurate engineering structure assessment, it is crucial to separate sensor behavior from structural influences using signal energy in the time domain. This study underscores the need to compensate for systematic effects, preventing the underestimation of vibration energy at low temperatures and overestimation at higher temperatures when using IEPE sensors for engineering structure monitoring.

The growing significance of wind energy in supplying renewable electricity underlines the increasing importance of wind turbine efficiency. Hybrid towers, integrating steel and pre-stressed concrete in a stacked structure, address traditional limitations in nacelle height but face new vulnerabilities, exemplified by a collapse in September 2021. This highlights the crucial need for continuous monitoring, particularly of the tower structure’s tendons. This study introduces acoustic emission monitoring as a novel approach for the early detection of wire breaks within the highly stressed tendons of hybrid towers. The investigations described focus on evaluating the suitability of this method for the specific use case and developing a generalized monitoring approach. Accordingly, background noise in an operating wind turbine tower was recorded and analyzed over a year-long operational period. Correlation analyses of these data unveiled intricate relationships between operational parameters and noise levels, with wind speed, rotor speed, and blade pitch angle exerting influence. Laboratory experiments were conducted on a full-scale specimen, and wire breaks were artificially provoked to characterize the damage signal and assess its attenuation in relevant structural components. The experimental results were integrated into a stochastic model to determine feasible sensor distances, aiming for a 90% probability of detection at a 95% confidence level. Low attenuation along the tendon was identified, enabling reliable detection over significant distances. Nevertheless, practical considerations suggest a focus on tendon anchorages, with the potential for grouped monitoring in specific areas to optimize sensor deployment. The study proposes a sensor network configuration to enhance the safety and reliability of wind turbine structures.

In the wind industry, recent advancements have led to the research and development of wind turbines featuring hybrid towers, a novel combination of steel tube and precast concrete segments. These concrete modules, which absorb the high stresses caused at the intersection between the tower and its foundation, are commonly joined with post-tensioned tendons. This study focuses on the identification of wire breaks in post-tensioned tendons through the application of acoustic emission testing. Measurements involve the evaluation of wire breaks under controlled laboratory conditions and the analysis of the dynamic operational environment of a functioning wind turbine. The investigation encompasses the development and assessment of a methodology based on principles of discrete Fourier transformation and Parseval’s theorem for analyzing the frequency-dependent energy distribution of acoustic signals. This analytical approach extends to the examination of wire breaks generated under varying stress levels as well as the operational noise within the actual wind turbine. The findings demonstrate a notable concentration of energy within the frequency range of 5-20 kHz in the wire break signals. In contrast, the operational noise signals show a distinct pattern, presenting an extreme value distribution within the 0-2 kHz range. The developed methodology exhibits robustness, even in the presence of high background noise levels, establishing itself as a valuable tool for wire break detection. Additionally, its potential for integration with machine learning algorithms offers the prospect of automated wire break detection, enabling further advancements in structural health monitoring for wind turbines.