Non-destructive testing on wire rope is in great demand to prevent safety accidents at sites where many heavy equipment using ropes are installed. In this paper, a research on quantification of magnetic flux leakage (MFL) signals were carried out to detect damages on wire rope. First, a simulation study was performed with a steel rod model using a finite element analysis (FEA) program. The leakage signals from the simulation study were obtained and it was compared for parameter: depth of defect. Then, an experiment on same conditions was conducted to verify the results of the simulation. Throughout the results, the MFL signal was quantified and a wire rope damage detection was then confirmed to be feasible. In further study, it is expected that the damage characterization of an entire specimen will be visualized as well.
In this study, a noncontact main cable NDE method has been developed. This cable NDE method utilizes the direct current (DC) magnetization and a searching coil-based total flux measurement. A total flux sensor head prototype was fabricated that consists of an electro-magnet yoke and a searching coil sensor. To obtain a B-H loop, a magnetic field was generated by applying a cycle of low frequency direct current to the electro-magnet yoke. During the magnetization, a search coil sensor measures the electromotive force from magnetized cable. During the magnetization process, a search coil sensor was measured the magnetic flux density. Total flux was calculated by integrating the measured magnetic flux using a fluxmeter. A B-H loop is obtained by using relationship between a cycle of input DC voltage and measured total flux. The B-H loop can reflect the property of the ferromagnetic materials. Therefore, the cross-sectional loss of cable can be detected using variation of features from the B-H curve. To verify the feasibility of the proposed steel cable NDE method, a series of experimental studies using a main-cable mock-up specimen has been performed in this study.
Recently, novel methods to estimate the strength of concrete have been reported based on numerous NDT methods. Especially, electro-mechanical impedance technique using piezoelectric sensors are studied to estimate the strength of concrete. However, the previous research works could not provide the general information about the early-age strength important to manage the quality of concrete and/or the construction process. In order to estimate the early-age strength of concrete, the electro-mechanical impedance method and the artificial neural network(ANN) is utilized in this study. The electro-mechanical impedance varies with the mechanical properties of host structures. Because the strength development is most influential factor among the change of mechanical properties at early-age of curing, it is possible to estimate the strength of concrete by analyzing the change of E/M impedance. The strength of concrete is a complex function of several factors like mix proportion, temperature, elasticity, etc. Because of this, it is hard to mathematically derive equations about strength of concrete. The ANN can provide the solution about early-age strength of concrete without mathematical equations. To verify the proposed approach, a series of experimental studies are conducted. The impedance signals are measured using embedded piezoelectric sensors during curing process and the resonant frequency of impedance is extracted as a strength feature. The strength of concrete is calculated by regression of strength development curve obtained by destructive test. Then ANN model is established by trained using experimental results. Finally the ANN model is verified using impedance data of other sensors.
In this research, a noncontact nondestructive testing (NDT) method is proposed to detect the fatigue crack and to identify the location of the damage. To achieve this goal, Lamb wave propagation of a plate-like structure is analyzed, which is induced by scanning laser source actuation system. A ND: YAG pulsed laser system is used to generate Lamb wave exerted at the multiple points of the plate and a piezoelectric sensor is installed to measure the structural responses. Multiple time signals measured by the piezoelectric sensor are aligned along the vertical and horizontal axes corresponding to laser impinging points so that 3 dimensional data can be constructed. Then, the 3 dimensional data is sliced along the time axis to visualize the wave propagation. The scattering of Lamb wave due to the damage can be described in the wave propagation image and hence the damage can be localized and quantified. Damage-sensitive features, which are reflected wave from the damage, are clearly extracted by wave-number filtering based on the 3 dimensional Fourier transform of the visualized data. Additional features are extracted by observing different scales of wavelet coefficients so that the time of flight (TOF) of Lamb wave modes can be clearly separated. Steel plates with fatigue cracks are investigated to verify the effectiveness and the robustness of the proposed NDT approach.
The steel cables in long span bridges such as cable-stayed bridges and suspension bridges are critical members which
suspend the load of main girders and bridge floor slabs. Damage of cable members can occur in the form of crosssectional
loss caused by fatigue, wear, and fracture, which can lead to structural failure due to concentrated stress in the
cable. Therefore, nondestructive examination of steel cables is necessary so that the cross-sectional loss can be detected.
Thus, an automated cable monitoring system using a suitable NDE technique and a cable climbing robot is proposed. In
this study, an MFL (Magnetic Flux Leakage- based inspection system was applied to monitor the condition of cables.
This inspection system measures magnetic flux to detect the local faults (LF) of steel cable. To verify the feasibility of
the proposed damage detection technique, an 8-channel MFL sensor head prototype was designed and fabricated. A steel
cable bunch specimen with several types of damage was fabricated and scanned by the MFL sensor head to measure the
magnetic flux density of the specimen. To interpret the condition of the steel cable, magnetic flux signals were used to
determine the locations of the flaws and the level of damage. Measured signals from the damaged specimen were
compared with thresholds set for objective decision making. In addition, the measured magnetic flux signal was
visualized into a 3D MFL map for convenient cable monitoring. Finally, the results were compared with information on
actual inflicted damages to confirm the accuracy and effectiveness of the proposed cable monitoring method.
During the construction of concrete structures, real-time monitoring for their strength development is very crucial to
determine the structures' readiness for in-service. However, it is very hard to estimate the compressive strength of the
concrete nondestructively and in real-time. To provide the solution for this limitation, this study proposes a guided wavebased
concrete strength estimation system using an embedded smart sensor module system. Because the guided waves
could not propagate clearly inside the concrete, an embedded smart sensor module system was developed by attaching
two piezoelectric ceramic sensors on a thin steel plate that could provide a propagating path for the guided waves.
Because the boundary condition of the steel plate is changed according to the variation of the concrete strength, the
guided wave signal obtained from the piezoelectric sensors might be changed with a certain pattern affected by the
boundary condition. Therefore, the strength of the concrete can be estimated by analyzing the pattern-variations of the
guided wave signals. To confirm the feasibility of the proposed methodology, an experimental study using a concrete
specimen with the aforementioned embedded smart sensor module system is carried out and the optimized strength
estimation equation is derived throughout a regression analysis.
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