Search

Under the working conditions of aero‐engine, the three‐dimensional composite deformation of the blade is caused by the centrifugal force generated by high‐speed rotation and the load generated by gas scouring. Aiming at the problem of three‐dimensional composite deformation on the surface of aero‐engine blades under high‐speed rotation, a three‐dimensional deformation measurement method based on stroboscopic structure digital image correlation method is proposed, and a method for eliminating rigid body displacement induced by the trigger error of the stroboscope is proposed. First, the laser trigger‐stroboscopic irradiation system is used to freeze the high‐speed rotating blade under a specific phase. Then, the binocular vision system is used to collect the surface image of the rotating blade. Finally, the digital image correlation method is used to measure the surface deformation field of the high‐speed rotating blade. In this paper, the experimental platform is built and the displacement field and main strain field of the blade surface under high‐speed rotating state are measured. The experimental results show that the stroboscopic structure digital image correlation measurement system and method have realized displacement measurement accuracy of 2 μm and strain measurement accuracy of 100 μɛ and have realized the surface deformation measurement of the ducted turbine fan blade under the condition of 6000 rpm (CFM56‐7 engine N1 speed). The results show that the method is effective and can provide a reliable measurement method for the deformation measurement of high‐speed rotating blades in large engine manufacturing enterprises such as Aero Engine Corporation of China. [ABSTRACT FROM AUTHOR]

Summary: The proportion of groundwater‐related diseases in the operational high‐speed railway tunnels in China is increasing and rapid and accurate disease monitoring is urgently needed. Therefore, it is a new attempt to monitor 5‐ to 10‐m water‐bearing anomalies in operating tunnel structures by using train‐borne transient electromagnetic method. The stationary interference source train tracks in the tunnel environment have serious electromagnetic interference to the monitoring of water‐bearing anomalies. Thus, this paper combines numerical simulations and indoor experiments to carry out research on the minimum water‐bearing anomalies that can be monitored at such close distances by train‐borne transient electromagnetic method under electromagnetic interference from train tracks. First, a comparative study of the electromagnetic characteristics of the water‐bearing anomalies when their own parameters change is carried out by numerical simulation, followed by a comparative analysis of the electromagnetic response of the water‐bearing anomalies under the interference of train tracks. For the missing electromagnetic response of the coil‐metal‐anomaly in the numerical simulation, a full‐scale physical model experiment is conducted to obtain the time range of the three interferences. Also in the experiments, the feasibility of existing instruments for close range water‐bearing anomalies detection is verified by comparing different transient electromagnetic instruments. Finally, the minimum water‐bearing anomalies that can be monitored in close proximity under the interference of train tracks are obtained experimentally. [ABSTRACT FROM AUTHOR]

Summary: Vibration‐based structural health monitoring for arch dams is influenced by various environmental effects, such as water depth and air temperature. These effects do not cause structural damage; therefore, their effects need to be minimized or removed from models. Moreover, arch dams are often located in earthquake‐prone areas; thus, an improved approach is proposed herein to adapt to the aforementioned situations. In this study, an arch dam in China was used for a case study. A long‐term continuous structural health monitoring system was proposed to operate under seismic and environmental excitation. An autoregressive exogenous model was applied for modal identification under seismic excitation. An automatic procedure based on stochastic subspace identification and density‐based spatial clustering of applications with noise was carried out for modal identification under environmental excitation. This study focused on the identification of the first three‐order modes. The seismic events were then analyzed. The natural frequencies before, during, and after seismic events were the same within a certain margin of error. This indicates that earthquakes did not cause major seismic damage to the arch dam. In addition, natural frequencies were continuously identified under environmental excitation. The relationship between natural frequency and water depth forms a quadratic curve, and the modal damping ratio increases with increasing water depth. Finally, this paper presents a random forest model to minimize environmental effects on natural frequencies. After minimizing the influence of non‐structural factors, the natural frequency exhibited a weak increasing trend over time. [ABSTRACT FROM AUTHOR]

Summary: Structural health monitoring (SHM) has been widely employed to reveal the hidden safety information and to diagnose the safety status in dam engineering fields. As one of the most important parameters of SHM, crack opening displacement (COD) is often used to evaluate the cracks or joints of concrete dams. In this paper, a new dam health analytic perspective is introduced by integrating the data mining method into SHM field, focusing on revealing the association rules in COD monitoring data. The association rules are investigated systematically, considering the cause–effect relations between external loads and structural response, the temporal characteristics of time series for a single sensor, the spatial characteristics of monitoring data for multisensors, and the abnormal characteristics for different items of structural responses. The association relation is quantified by proposing the quantitative indexes, including support degree, confidence degree, and promotion degree. The methods are used in the COD monitoring data of the Baishan concrete gravity‐arch dam, which is located in a severely cold area in northeastern China. Results show that 4 out of 24 cause–effect association rules are extracted by calculating the association degree of monitored COD values, and 21 out of 24 crack sensors present a temporal association relationship, among which the confidence degree of two sensors reaches 100%. The variation trend of COD values is relevant with the locations of the crack sensors. These results are consistent with the dam safety monitoring theories and models, which would be very useful for extracting the SHM information between different sensors, predicting the trend of COD value and repairing the monitoring data series of COD sensors, or even for discovering an abnormal signal for the operation safety of dams. [ABSTRACT FROM AUTHOR]