• Smart Structures and Systems
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• v.12 no.3_4
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• pp.381-397
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• 2013

In this paper, wireless health monitoring of stay cables using piezoelectric strain sensors and a smart skin technique is presented. For the cables, tension forces are estimated to examine their health status from vibration features with consideration of temperature effects. The following approaches are implemented to achieve the objective. Firstly, the tension force estimation utilizing the piezoelectric sensor-embedded smart skin is presented. A temperature correlation model to recalculate the tension force at a temperature of interest is designed by correlating the change in cable's dynamic features and temperature variation. Secondly, the wireless health monitoring system for stay cables is described. A piezoelectric strain sensor node and a tension force monitoring software which is embedded in the sensor are designed. Finally, the feasibility of the proposed monitoring technique is evaluated on stay cables of the Hwamyung Grand Bridge in Busan, Korea.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.829-834
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• 2004

This research is to development advanced health(condition) monitoring system of superconducting motors. Development of advanced condition monitoring systems offers the prospect of improved performance, assessment, and operation, simplified design, enhanced safety, and reduced overall cost of advanced and next generation superconducting motor. For advanced and next generation superconducting motor design, the opportunity exists to develop and implement real-time and continuous monitoring systems by integrating wireless and computational technique. Generally, condition monitoring and control of temperature is essential for managing the superconducting motor components, rotor and structures. In this research, development of advanced monitoring in low temperature and high speed operating environments offers the potential to greatly improve the control of harsh environments. In conventional method, slip rings have been used to acquire data from these sensors. However, the increase of sensors leads to vibration of the rotation axis and noise signals due to kinematics contact. In this study, the wireless data acquisition technique was employed to develop more stable monitoring system adequate for high speed rotating system.

• Smart Structures and Systems
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• v.9 no.6
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• pp.473-487
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• 2012

GPS has become an established technique in structural health monitoring. This paper presents the application of an on-line GPS RTK system on the Zhujiang Huangpu Bridge (China) for monitoring bridge deck and towers movements. In this study, both the form and functions of movements of the deck and towers of the bridge under affecting loads were monitored in lateral, longitudinal and vertical directions. Such movements were described in time and frequency domains by determining the trend, torsion, periodical of the series using probability density function (PDF). The results of the time series GPS data are practical and useful to bridge health monitoring.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• v.y2005m4
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• pp.248-251
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• 2005

In this paper, health monitoring technique has been studied for performance deterioration caused by the defects of the gas turbine. The parameters for performance diagnostics have been extracted by using GSP program for modeling the target engine. The virtual sensor model for the health monitoring has been built of those data. The position and magnitude of the defects of the engine components have been determined by using Multiple Linear Regression technique and the method using the weight in order to diagnose the single and multiple defects.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.3
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• pp.241-262
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• 2012

Some applications require structural health monitoring in inaccessible components. This paper presents a technique useful for Structural Health Monitoring of double wall structures, such as double wall steam pipes and double wall pressure vessels separated from an ultrasonic transducer by three layers. Detection has been demonstrated at distances in excess of one meter for a fixed transducer. The case presented here is for one of the layers, the middle layer, being a fluid. For certain transducer configurations the wave propagating in the fluid is a wave with low velocity and attenuation. The paper presents a model based on wave theory and finite element simulation; the experimental set-up and observations, and comparison between theory and experiment. The results provide a description of the technique, understanding of the phenomenon and its possible applications in Structural Health Monitoring.

• Smart Structures and Systems
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• v.20 no.2
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• pp.163-173
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• 2017

Structural health monitoring (SHM) of civil infrastructure using fiber Bragg grating sensor networks (FBGSNs) has received significant public attention in recent years. However, there is currently little research on the health-monitoring technology of high-piled wharfs in coastal ports using the fiber Bragg grating (FBG) sensor technique. The benefits of FBG sensors are their small size, light weight, lack of conductivity, resistance corrosion, multiplexing ability and immunity to electromagnetic interference. Based on the properties of high-piled wharfs in coastal ports and servicing seawater environment and the benefits of FBG sensors, the SHM system for a high-piled wharf in the Tianjin Port of China is devised and deployed partly using the FBG sensor technique. In addition, the health-monitoring parameters are proposed. The system can monitor the structural mechanical properties and durability, which provides a state-of-the-art mean to monitor the health conditions of the wharf and display the monitored data with the BIM technique. In total, 289 FBG stain sensors, 87 FBG temperature sensors, 20 FBG obliquity sensors, 16 FBG pressure sensors, 8 FBG acceleration sensors and 4 anode ladders are installed in the components of the back platform and front platform. After the installation of some components in the wharf construction site, the good signal that each sensor measures demonstrates the suitability of the sensor setup methods, and it is proper for the full-scale, continuous, autonomous SHM deployment for the high-piled wharf in the costal port. The South 27# Wharf SHM system constitutes the largest deployment of FBG sensors for wharf structures in costal ports to date. This deployment demonstrates the strong potential of FBGSNs to monitor the health of large-scale coastal wharf structures. This study can provide a reference to the long-term health-monitoring system deployment for high-piled wharf structures in coastal ports.

• Smart Structures and Systems
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• v.17 no.5
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• pp.795-817
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• 2016

A non-contact, in-situ and non-invasive technique for health monitoring of submerged fiber reinforced polymers (FRP) laminates has been developed using ultrasonic guided waves. A pair of mobile transducers at specific angles of incidence to the submerged FRP specimen was used to excite Lamb wave modes. Lamb wave modes were used for comprehensive inspection of various types of manufacturing defects like air gaps and missing epoxy, introduced during manufacturing of FRP using Vacuum Assisted Resin Infusion Molding (VARIM). Further service induced damages like notches and surface defects were also studied and evaluated using guided waves. Quantitative evaluation of transmitted ultrasonic signal in defect ridden FRPs $vis-{\grave{a}}-vis$ healthy signal has been used to relate the extent of damage in FRPs. The developed technique has the potential to develop into a quick, real time health monitoring tool for judging the service worthiness of FRPs.

• Journal of Wind Energy
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• v.9 no.4
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• pp.16-23
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• 2018

A damage detection method for the tripod support structure of offshore wind turbines is presented for structural health monitoring. A finite element model of a prototype tripod support structure is established and the modal properties are calculated. The degree and location of the damage are estimated based on the neural network technique using the changes of natural frequencies and mode shape due to the damage. The stress distribution occurring in the support structure is obtained by a dynamic analysis for the wind turbine system to select the output data of the neural network. The natural frequencies and mode shapes for 36 possible damage scenarios were used for the input data of the learned neural network for damage assessment. The estimated damages agreed reasonably well with the accurate ones. The presented method could be effectively applied for damage detection and structural health monitoring of various types of support structures of offshore wind turbines.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.49-52
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• 2007

Terrestrial Laser Scanning(TLS) was developed at the mid-to-late 1990s. This technique enables to perform reconnaissance surveying of regions or structures hard to access. Besides, TLS has been extended its application gradually such as preservation of historical remains, underground surveys, slopes, glaciers monitoring and so on. However, though the technique has a lot of advantages, an application for structural health and safety monitoring is a beginning stage and it need much research. Therefore in this study, as a groundwork, the estimation model on stress of structures using TLS and Finite Element Method(FEM) applied by the Digital Elevation Model(DEM) technique of geoinformatics is proposed. For the verification of this model, experiments were performed with a continuous steel beam subjected to point loads and outputs were compared with those of electrical strain sensors.

• Structural Engineering and Mechanics
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• v.23 no.1
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• pp.75-95
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• 2006

Two-step identification approaches for effective bridge health monitoring are proposed to alleviate the issues associated with many unknown parameters faced in real structures and to improve the accuracy in the estimate results. It is suitable for on-line monitoring scheme, since the damage assessment is not always needed to be carried out whereas the alarming for damages is to be continuously monitored. In the first step for screening potentially damaged members, a damage indicator method based on modal strain energy, probabilistic neural networks and the conventional neural networks using grouping technique are utilized and then the conventional neural networks technique is utilized for damage assessment on the screened members in the second step. The effectiveness of the proposed methods is investigated through a field test on the northern-most span of the old Hannam Grand Bridge over the Han River in Seoul, Korea.