• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.602-607
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• 2003

Sensors based bridge monitoring system (SBBMS) is designed to perform real-time monitoring and to store the performance history of in-service bridges. In general, visual inspections play a major role in maintenance of in-service bridges; however, they are not adequate to document the behavior of a bridge. Therefore, visual inspections and sensor based monitoring systems complement each other. Sensor based bridge monitoring systems consist of hardware and software systems. The hardware system contains the sensors and data-loggers to measure the behavior of a structure, the communicational equipment to transmit the measured data from the site to the monitoring center, and the computers to arrange and analyze the data. The software system controls data-loggers, arranges and analyzes the measured data, makes real-time display, stores the performance history.

• Smart Structures and Systems
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• 제22권2호
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• pp.231-237
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• 2018

A significant data problem is encountered with condition monitoring because the sensors need to measure vibration data at a continuous and sometimes high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer data than traditional data sampling methods. This sensing paradigm is applied to condition monitoring with an improved machine learning algorithm in this study. For the experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. The optimal signal features were then selected without the signal reconstruction process. For damage classification, we used the Variance Considered Machine, utilizing only the compressed data. The experimental results show that the proposed compressive sensing method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• 한국해양공학회지
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• 제12권2호통권28호
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• pp.139-146
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• 1998

This paper introduces a network system for monitoring coastal oceanographic data. The network system consists of three parts such as the buoy to observe oceanographic data, the local site to collect data transferred from buoys, and the host site to construct the oceanographic data-base and to share the information for monitoring coastal oceanographic data. The buoy has a one-board microcomputer to manage and to acquire coastal environment data in real-time. A wireless and wire communication technique is employed in order to transfer data measured by buoys and to link local and host sites, respectively. In measuring coastal environment data, this system shows more cost-effective way than the presents conventional. In addition, the realtime monitoring system continuously from various sites with the network systems.

• Computers and Concrete
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• 제20권5호
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• pp.555-562
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• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.569-575
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• 2022

This is a research thesis on the development of a monitoring and diagnosis device that prevents the risk of an accident through monitoring and diagnosis of a modular Main Circuit Breaker (MCB) using Vacuum Interrupter (VI) for Korean high-speed rolling stock. In this paper, a comprehensive MCB monitoring and diagnosis was performed by converging vacuum level diagnosis of interrupter, operating coil monitoring of MCB and environmental temperature/humidity monitoring of modular box. In addition, to develop an algorithm that is expected to have a similar data processing before the actual field test of the MCB monitoring and diagnosis device in 2023, the cluster analysis and factor analysis were performed using the WEKA data mining technique on the big data of Korean railroad transformer, which was previously researched by Tae Hee Evolution with KORAIL.

• Steel and Composite Structures
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• 제49권6호
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• pp.701-711
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• 2023

In order to fully reflect variation characteristics of composite concrete dam health state, the monitoring data is applied to diagnose composite concrete dam health state. Composite concrete dam lesion development to wreckage is a precursor, and its health status can be judged. The monitoring data are generally non-linear and unsteady time series, which contain chaotic information that cannot be characterized. Thus, it could generate huge influence for the construction of monitoring models and the formulation of corresponding health diagnostic indicators. This multi-scale diagnosis process is from point to whole. Chaotic characteristics are often contained in the monitoring data. If chaotic characteristics could be extracted for reflecting concrete dam health state and the corresponding diagnostic indicators will be formulated, the theory and method of diagnosing concrete dam health state can be huge improved. Therefore, the chaotic characteristics of monitoring data are considered. And, the extracting method of the chaotic components is studied from monitoring data based on fuzzy dynamic cross-correlation factor method. Finally, a method is proposed for formulating composite concrete dam health state indicators. This method can effectively distinguish chaotic systems from deterministic systems and reflect the health state of concrete dam in service.

• Smart Structures and Systems
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• 제24권6호
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• pp.693-707
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• 2019

This paper proposes an integrated safety assessment method that can take multiple sources data into consideration based on a data fusion approach. Data cleaning using the Kalman filter method (KF) was conducted first for monitoring data from each sensor. The inclination data from the four tilt sensors of the same monitoring section have been associated to synchronize in time. Secondly, the finite element method (FEM) model was established to physically correlate the external forces with various structural responses of the shield tunnel, including the measured inclination. Response surface method (RSM) was adopted to express the relationship between external forces and the structural responses. Then, the external forces were updated based on the in situ monitoring data from tilt sensors using the extended Kalman filter method (EKF). Finally, mechanics parameters of the tunnel lining were estimated based on the updated data to make an integrated safety assessment. An application example of the proposed method was presented for an urban tunnel during a nearby deep excavation with multiple source monitoring plans. The change of tunnel convergence, bolt stress and segment internal forces can also be calculated based on the real time deformation monitoring of the shield tunnel. The proposed method was verified by predicting the data using the other three sensors in the same section. The correlation among different monitoring data has been discussed before the conclusion was drawn.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.1097-1100
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• 2003

Data Loggers for environmental monitoring are mostly dispersion in installation and systems located at long distance from monitoring system. And, it requests mostly flexible functions and high performances. that can fit to various sensor inputs, sensor interfaces and conditions or system working. In this research, we developed the micro controller based Data Logger with minimum hardware construction that allows the higher flexibility of application. Finally, we developed software function for water quality monitoring and tested in real system launched at Han river.

• Smart Structures and Systems
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• 제21권5호
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• 한국물환경학회지
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• 제29권1호
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• pp.55-65
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• 2013

It is very difficult to apply stream flow data directly to the management of Total Maximum Daily Loads because there are some differences between the unit watershed and the stream flow monitoring network in their characteristics such as monitoring locations and its intervals. Flow duration curve can be developed by linking the daily flow data of stream monitoring network to 8 day interval flow data of the unit watershed. This study investigated the current operating conditions of the stream flow monitoring network and the flow relationships between the unit watershed and the stream flow monitoring network. Criteria such as missing and zero value data, and correlation coefficients were applied to select the stream flow reference sites. The reference sites were selected in 112 areas out of 142 unit watersheds in 4 river basins, where the stream flow observations were carried out in relatively normal operating conditions. These reference sites could be utilized in various ways such as flow variation analysis, flow duration curve development and so on for the management of Total Maximum Daily Loads.