Acoustic emission is known as a powerful nondestructive tool to detect any further growth of preexisting cracks or to characterize failure mechanisms. Recently, this kind of technique, which is an in situ monitoring of integrity of materials or structures, becomes increasingly popular for monitoring the conditions of large structures such as a wind turbine blade. Therefore, it is required to find a symptom of damage progress before catastrophic failure through a continuous monitoring. In this study, acoustic emission technology was applied to assess the damage in the wind turbine blade during step-by-step static load test. In this static loading test, we have used a full-scale blade of 100 kW in capacity, and an attempt was made to apply a new source location method using a new algorithm with energy contour mapping concept. We also measured the deflection of blade tip by linear variable differential transformer (LVDT) and the strain of inner shear web in order to analyze the correlation between stress condition and damage identification. The results show that the acoustic emission activities give a good agreement with the stress distribution and damage location in the blade, especially in bonding edges around 1000–1500 mm far from the root. Finally, the applicability of new source location method was confirmed by comparison of the result of source location and experimental damage location.

We propose a weighted singular value decomposition (WSVD) to reduce the random noise in the Rayleigh backscattering signal of the phase-sensitive optical time domain reflectometry (Φ-OTDR) to enhance the detection performance of the distributed vibration sensing. A 2D image is formed by assembling the raw Rayleigh backscattering traces into a matrix form, and slowly varying fluctuation and random noise can be removed using the WSVD. Consequently, the location information and the frequency of vibration induced by the external vibration event can be extracted. A vibration event with 9 m spatial resolution is detected along a 2.4 km single mode fiber. The signal-to-noise ratio (SNR) of location information for the 102 Hz physical vibration and the 525 Hz acoustic vibration was found to be 10.7 and 12.2 dB, respectively. The SNR of the vibration events demonstrate an increase of 6–7 dB compared to the conventional method, showing the excellent denoising capability of this new approach.

Anchor systems are widely used to stabilize soil slope and suppress slope failure. Thus, monitoring conditions of an anchor system is important to prevent disasters due to slope failure. The slope condition can be indirectly monitored by sensing the tensile force applied to the anchor because the slope deformation directly affects the anchor force. Previously, we propose a way to monitor the tensile force of the anchor by measuring the strain field on a bearing plate using a distributed fiber optic sensor (OFDR) and experimentally demonstrate that the anchor force has a large correlation with the strain distribution on the bearing plate. However, it was found that a spatial variation of the strain and thermal strain due to temperature change makes it difficult to get a reliable correlation coefficient. In this study, we newly propose a way to get a reliable correlation coefficient between the anchor force and the strain field on the bearing plate. We install a distributed optical fiber sensor in two concentric circles on the bearing plate and measure circumferential strain distribution. We take average values of the strain field in each circle as representative strain values minimizing the spatial variation and takes a difference of the two strains to exclude the temperature effect. We experimentally demonstrate that the proposed method gives a reliable correlation coefficient between the anchor force and the strain field on the bearing plate. This technique can be applied to various anchor systems to monitor the anchor force and manage the anchor systems safely.

Big impacts from pressure transients are known to be major contributors to pipeline failures, and even small impacts have the potential to result in pipeline deterioration. The effects of these impacts on a pipeline are not disclosed in detail and are hard to evaluate completely by theoretical modeling or numerical simulation. The impacts excite cylindrical structures containing liquid, which results in the development of wave propagation along pipelines. In particular, quasi-longitudinal waves are known to be developed by fluid-structure interaction during propagation. However, impact signal detection needs great care because extreme noise may corrupt the signal. An enhanced wavelet-based approach is proposed to detect and localize the impact source. The method makes use of continuous wavelet transform and band summation within a band of interest along scales to enhance time-difference detectability. In addition, a noise reduction algorithm intended to remove burst noises, in practice, frequently contaminates the impact signals of interest. Experimental results from a water supply network under operation demonstrate that the proposed approach is able to suppress noise and successfully reveal the impact location. The proposed approach provides a more precise and robust way to localize impacts missed by the conventional cross-correlation algorithm.

The failure of buried pipelines can lead to serious consequences such as explosions, environmental pollution, settlement, as well as economic loss. To prevent these outcomes, it is crucial to identify the causes of failure and monitor their signs. One of the main causes of failure is unexpected third-party interference (TPI), which is particularly challenging to detect. Regarding to this issue, this study proposes a new algorithm for monitoring impact damage, which can be used for prompt response and damage prevention. The algorithm is integrated into the system using two approaches. The first approach focuses on detecting the location of the damage (referred to as source location). A kurtosis-based transfer function was newly proposed to selecting the optimal frequency band for time-difference-of-arrival based source location, resulting in accurate pinpointing of damage, even in a noisy environment. The second approach is used to determine whether impact damage has actually occurred by observing newly suggested features in both the time and frequency domains (referred to as anomaly detection). These features evaluate the presence of damage and the similarity between signals. As a result, it was evaluated that the field applicability was higher than that of conventional methods, and the superiority of the proposed method was also verified through field experiments. The method proposed in this study is expected to enable immediate response when the integrity of the buried pipelines is on the line of failure due to TPI.

Buried pipelines, such as waterworks pipelines, are critical for transmitting essential resources and energy in modern cities, but the risk of pipeline failure, especially due to third-party interference, is a major concern. While various studies have focused on leak detection in waterworks pipelines, research on preventing impact damage is limited. To address this issue, this study proposes a novel algorithm that utilizes energy and similarity measurements for impact detection and compares it theoretically to existing leak-detection methods. The proposed algorithm utilizes frequency-domain kurtosis to determine the frequency band on which the energy of the impact signals is concentrated, along with a time-shift coherence function to measure the similarity of the signals. The application of the source location using the filtered signals enables accurate detection of the location of third-party interference. The proposed algorithm aims to ensure the safety and to prevent failures of buried pipelines. To verify the feasibility of the proposed algorithm, an excavation experiment using a backhoe was conducted on an in-service waterworks pipeline with a diameter of 2200 mm and a burial depth of 3 m. This experiment confirmed the effectiveness of the proposed algorithm in preventing failures of buried pipelines and demonstrated its practical applicability in the field. The experiment also validated the algorithm’s ability to detect third-party interference damage at various points.