• Smart Structures and Systems
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• v.17 no.4
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• pp.631-646
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• 2016

Although the deployment of wireless sensors for structural sensing and monitoring is becoming popular, supplying power to these sensors remains as a daunting task. To address this issue, there have been large volume of ongoing energy harvesting studies that aimed to find a way to scavenge energy from surrounding ambient energy sources such as vibration, light and heat. In this study, a magnetic resonance based wireless power transfer (MR-WPT) system is proposed so that sensors inside a concrete structure can be wirelessly powered by an external power source. MR-WPT system offers need-based active power transfer using an external power source, and allows wireless power transfer through 300-mm thick reinforced concrete with 21.34% and 17.29% transfer efficiency at distances of 450 mm and 500 mm, respectively. Because enough power to operate a typical wireless sensor can be instantaneously transferred using the proposed MR-WPT system, no additional energy storage devices such as rechargeable batteries or supercapacitors are required inside the wireless sensor, extending the expected life-span of the sensor.

• Journal of Ocean Engineering and Technology
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• v.20 no.2 s.69
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• pp.1-7
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• 2006

This paper, presents a comparison between numerical analysis and field test on a real offshore platform in Bohai Bay, China. This platform is a steel jacket offshore platform with vertical piles. The field testing under wave-induced force and wind force etc. was conducted, in order to obtain the dynamic parameters of the structure, including the frequencies of the jacket platform, as well as the corresponding damping ratios and mode shapes. The natural excitation technology (NexT) combined with eigensystem realization algorithm (ERA) and the peak picking (PP) method in frequency domain are carried out for modal parameter indentification under operational conditions. The three-dimeansional finite element model (FEM) is constructed by ANSYS and analytical modal analysis is performed to generate modal parameters. The analytical results were compared with experimental results. A good agreement was achieved between the finite element and analysis and field test results. It is further demonstrated that the numerical and experimental modal analysis provide a comprehensive study on the dynamic properties of the jacket platform. According to the analysis results, the modal parameters identification under ambient excitation can calibrate finite element model of the jacket platform structures, or can be used for the structural health monitoring system.

• Smart Structures and Systems
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• v.9 no.4
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• pp.335-353
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• 2012

This study suggests a simple two-step method for structural vibration-based health monitoring for beam-like structures which only utilizes mode shape curvature and few natural frequencies of the structures in order to detect and localize cracks. The method is firstly based on the application of wavelet transform to detect crack locations from mode shape curvature. Then particle swarm optimization is applied to evaluate crack depth. As the Rayleigh quotient is introduced to estimate natural frequencies of cracked beams, the relationship of natural frequencies and crack depths can be easily obtained with only a simple formula. The method is demonstrated and validated numerically, using the numerical examples (cantilever beam and simply supported shaft) in the literature, and experimentally for a cantilever beam. Our results show that mode shape curvature and few estimated natural frequencies can be used to detect crack locations and depths precisely even under a certain level of noise. The method can be extended for health monitoring of other more complicated structures.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.57-62
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• 2019

In this paper, oneM2M based data monitoring and acquisition system is designed and implemented to measure and transmit the voltage, current, temperature, acceleration and vibration of the motor. The proposed system can detect electrical faults (overcurrent, reverse phase, phase loss, ground fault) and mechanical faults (MC counter, motor operation time, bearing and winding temperature, motor speed, insulation resistance). The system consists of sensor data collection, web server, php, database, wired/wireless communication system. The insulation resistance and the motor speed were measured, and the experimental results were similar for both the test resistance value and the reference input value.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1055-1056
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• 2007

Asymmetric electro-magnetic force caused by the frictional worn bearing, rotor misalignment and unbalanced rotor etc. generates an asymmetrical operation, vibration and electro-magnetic noise. The need for detection of these rotor eccentricities has pushed the development of monitoring methods with increasing sensitivity and noise immunity. This paper is proposed the analysis method of the squirrel-cage induction motor driven by IGBT inverter using finite element method (FEM) and subroutine. The effect of the unbalanced magnetic pull in the inverter-fed induction motor which is in asymmetrical whirling motion is presented. The analysis results of rotor eccentricity could compare with motors which have been made normal air-gap motor and irregular air-gap motor and verify reliability. The simulation and experiment results can be useful for on-line faults detection monitoring system of induction motors.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.5
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• pp.9-13
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• 2009

Modal parameters including resonant frequencies and mode shapes are heavily utililized in most damage identification throries for structural health monitoring. However, extracting modal parameters from dynamic responses needs postprocessing which inevitably involves errors in curve-fitting resonants as well as transforming the domain of responses. In this paper, the applicability of a damage identification method based on free vibration responses to the in-sevice responses is experimentally verified. The experiment is performed via applying periodic and nonperiodic moving loads to a simply supported beam and displacement responses are measured. The moving load is simulated using steel balls and a downhill device. The damage identification results show that the in-service response may be applicable to identifying damage in the beam.

• Structural Engineering and Mechanics
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• v.71 no.2
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• pp.175-183
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• 2019

This paper deals with damage detection in a girder bridge using transmissibility functions as input data to Artificial Neural Networks (ANNs). The original contribution in this work is that these two novel methods are combined to detect damage in a bridge. The damage was simulated in a real bridge in Vietnam, i.e. Ca-Non Bridge. Finite Element Method (FEM) of this bridge was used to show the reliability of the proposed technique. The vibration responses at some points of the bridge under a moving truck are simulated and used to calculate the transmissibility functions. These functions are then used as input data to train the ANNs, in which the target is the location and the severity of the damage in the bridge. After training successfully, the network can be used to assess the damage. Although simulated responses data are used in this paper, the practical application of the technique to real bridge data is potentially high.

• Structural Engineering and Mechanics
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• v.77 no.4
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• pp.495-508
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• 2021

This study devises a novel approach, namely quadruple 1D convolutional neural network, for detecting connection stiffness reduction in steel truss bridge structure using experimental and numerical modal data. The method is developed based on expertise in two domains: firstly, in Structural Health Monitoring, the mode shapes and its high-order derivatives, including second, third, and fourth derivatives, are accurate indicators in assessing damages. Secondly, in the Machine Learning literature, the deep convolutional neural networks are able to extract relevant features from input data, then perform classification tasks with high accuracy and reduced time complexity. The efficacy and effectiveness of the present method are supported through an extensive case study with the railway Nam O bridge. It delivers highly accurate results in assessing damage localization and damage severity for single as well as multiple damage scenarios. In addition, the robustness of this method is tested with the presence of white noise reflecting unavoidable uncertainties in signal processing and modeling in reality. The proposed approach is able to provide stable results with data corrupted by noise up to 10%.

• Smart Structures and Systems
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• v.30 no.2
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• pp.183-193
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• 2022

Structural health monitoring and structural vibration control are multidisciplinary and frontier research directions of civil engineering. As intelligent materials that integrate sensing and actuation capabilities, shape memory alloys (SMAs) exhibit multiple excellent characteristics, such as shape memory effect, superelasticity, corrosion resistance, fatigue resistance, and high energy density. Moreover, SMAs possess excellent resistance sensing properties and large deformation ability. Superfine NiTi SMA wires have potential applications in structural health monitoring and micro-drive system. In this study, the mechanical properties and electrical resistance sensing characteristics of superfine NiTi SMA wires were experimentally investigated. The mechanical parameters such as residual strain, hysteretic energy, secant stiffness, and equivalent damping ratio were analyzed at different training strain amplitudes and numbers of loading-unloading cycles. The results demonstrate that the detwinning process shortened with increasing training amplitude, while austenitic mechanical properties were not affected. In addition, superfine SMA wires showed good strain-resistance linear correlation, and the loading rate had little effect on their mechanical properties and electrical resistance sensing characteristics. This study aims to provide an experimental basis for the application of superfine SMA wires in engineering.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.5
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• pp.433-441
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• 2023

Piezoelectric ceramics play an important role in electrical and electronic devices such as sensors, actuators, and microelectronic devices. However, traditional ceramics are difficult to be used in various process industries due to their high brittleness and low flexibility. Therefore, piezoelectric paint sensors have been designed for application to the curved surfaces of complicated structures. Furthermore, recently, significant attention has been focused on the development of paint sensors that can be used as structure health monitoring sensors for vibration, impact, and acoustic emission. Several studies have successfully demonstrated the possibility that smart paint sensors can take the place of traditional ceramic sensors. In this review, we briefly introduce the concept of the piezoelectric paint sensors and the expected application field as well as their preparation and history.