• Journal of the Korean Society for Railway
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• v.12 no.1
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• pp.129-134
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• 2009

To ensure the safety and functionality of a railroad bridge, maintaining the integrity of the bridge via continuous structural health monitoring is important. However, most structural integrity monitoring methods proposed to date are based on modal responses which require the extracting process and have limited availability. In this paper, the applicability of the existing damage identification method based on free-vibration reponses to time-domain deflection shapes due to moving train load is investigated. Since the proposed method directly utilizes the time-domain responses of the structure due to the moving vehicles, the extracting process for modal responses can be avoided, and the applicability of structural health evaluation can be enhanced. The feasibility of the presented method is verified via a numerical example of a simple plate girder bridge.

• Wind and Structures
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• v.20 no.5
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• pp.701-718
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• 2015

The joint distribution of wind speed and wind direction at a bridge site is vital to the estimation of the basic wind speed, and hence to the wind-induced vibration analysis of long-span bridges. Instead of the conventional way relying on the weather stations, this study proposed an alternate approach to obtain the original records of wind speed and the corresponding directions based on field measurement supported by the Structural Health Monitoring System (SHMS). Specifically, SHMS of Sutong Cable-stayed Bridge (SCB) is utilized to study the basic wind speed with directional information. Four anemometers are installed in the SHMS of SCB: upstream and downstream of the main deck center, top of the north and south tower respectively. Using the recorded wind data from SHMS, the joint distribution of wind speed and direction is investigated based on statistical methods, and then the basic wind speeds in 10-year and 100-year recurrence intervals at these four key positions are calculated. Analytical results verify the reliability of the recorded wind data from SHMS, and indicate that the joint probability model for the extreme wind speed at SCB site fits well with the Weibull model. It is shown that the calculated basic wind speed is reduced by considering the influence of wind direction. Compared to the design basic wind speed in the Specification of China, basic wind speed considering the influence of direction or not is much smaller, indicating a high safety coefficient in the design of SCB. The results obtained in this study can provide not only references for further wind-resistance research of SCB, but also improve the understanding of the safety coefficient for wind-resistance design of other engineering structures in the similar area.

• Wind and Structures
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• v.17 no.1
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• pp.43-68
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• 2013

The fluctuating wind induced vibration is one of the most important factors which has been taken into account in the design of long-span bridge due to the low stiffness and low natural frequency. Field measurement characteristics of sustained wind on structure site can provide accurate wind load parameters for wind field simulation and structural wind resistance design. As a suspension bridge with 1490 m main span, the Runyang Suspension Bridge (RSB) has high sensitivity to fluctuating wind. The simultaneous and continuously wind environment field measurement both in mid-span and on tower top is executed from 2005 up to now by the structural health monitoring system installed on this bridge. Based on the recorded data, the wind characteristic parameters, including mean wind speed, wind direction, the turbulence intensity, the gust factors, the turbulence integral length, power spectrum and spatial correlation, are analyzed in detail and the coherence functions of those parameters are evaluated using statistical method in this paper. The results indicate that, the turbulence component of sustain wind is larger than extremely strong winds although its mean wind speed is smaller; the correlation between turbulence parameters is obvious; the power spectrum is special and not accord with the Simiu spectrum and von Karman spectrum. Results obtained in this study can be used to evaluate the long term reliability of the Runyang Suspension Bridge and provide reference values for wind resistant design of other structures in this region.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.111-121
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• 2018

Many problems in rotating machinery such as aircraft engines, wind turbines and motors are caused by bearing defects. The abnormalities of the bearing can be detected by analyzing signal data such as vibration or noise, proper pre-processing through a few signal processing techniques is required to analyze their frequencies. In this paper, we introduce the condition monitoring method for diagnosing the failure of the rotating machines by analyzing the vibration signal of the bearing. From the collected signal data, the normal states are trained, and then normal or abnormal state data are classified based on the trained normal state. For preprocessing, a Hamming window is applied to eliminate leakage generated in this process, and the cepstrum analysis is performed to obtain the original signal of the signal data, called the formant. From the vibration data of the IMS bearing dataset, we have extracted 6 statistic indicators using the cepstral coefficients and showed that the application of the Mahalanobis distance classifier can monitor the bearing status and detect the failure in advance.

• Korean Chemical Engineering Research
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• v.60 no.2
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• pp.249-254
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• 2022

With the rapid advance of the semiconductor and Information and communication technologies, remote environment monitoring technology, which can detect and analyze surrounding environmental conditions with various types of sensors and wireless communication technologies, is also drawing attention. However, since the conventional remote environmental monitoring systems require external power supplies, it causes time and space limitations on comfortable usage. In this study, we proposed the concept of the self-powered remote environmental monitoring system by supplying the power with the levitation-electromagnetic generator (L-EMG), which is rationally designed to effectively harvest biomechanical energy in consideration of the mechanical characteristics of biomechanical energy. In this regard, the proposed L-EMG is designed to effectively respond to the external vibration with the movable center magnet considering the mechanical characteristics of the biomechanical energy, such as relatively low-frequency and high amplitude of vibration. Hence the L-EMG based on the fragile force equilibrium can generate high-quality electrical energy to supply power. Additionally, the environmental detective sensor and wireless transmission module are composed of the micro control unit (MCU) to minimize the required power for electronic device operation by applying the sleep mode, resulting in the extension of operation time. Finally, in order to maximize user convenience, a mobile phone application was built to enable easy monitoring of the surrounding environment. Thus, the proposed concept not only verifies the possibility of establishing the self-powered remote environmental monitoring system using biomechanical energy but further suggests a design guideline.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.84-89
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• 2019

This is a pilot study of machine learning based structural health monitoring system using flight data of composite aircraft. In this study, the most suitable machine learning algorithm for structural health monitoring was selected and dimensionality reduction method for application on the actual flight data was conducted. For these tasks, impact test on the cantilever beam with added mass, which is the simulation of damage in the aircraft wing structure was conducted and classification model for damage states (damage location and level) was trained. Through vibration test of cantilever beam with fiber bragg grating (FBG) sensor, data of normal and 12 damaged states were acquired, and the most suitable algorithm was selected through comparison between algorithms like tree, discriminant, support vector machine (SVM), kNN, ensemble. Besides, through neighborhood component analysis (NCA) feature selection, dimensionality reduction which is necessary to deal with high dimensional flight data was conducted. As a result, quadratic SVMs performed best with 98.7% for without NCA and 95.9% for with NCA. It is also shown that the application of NCA improved prediction speed, training time, and model memory.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.6
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• pp.147-154
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• 2014

The cable damages of the bridge structures induce very important impact on the structural safety, which implies the close monitoring of the cable damage is required to secure sustained safety of the bridges. Most usual available maintenance techniques are based on the monitoring the change of the natural frequency of the structures by damages. However, existing method are based on vibration method to calculate lateral vibration and system identification can calculate the axial stiffness using sensitivity equation by trial error method. But the frequency study by the longitudinal movement need because of the sag effect in system identification. This study proposes a new method to investigate the damage magnitudes and status. The method improves the accuracies in the magnitudes and status of damages by adopting the natural frequency of longitudinal movement. The study results have been validated by comparing them with the approximate solution of FEM. Thus, the relationship of cable damage and frequency appear with relation that the severe damage has the little frequency. If we know the real frequency we can estimate the cable damage severity using this relationship. This method can be possible the efficient management of the cable damage.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.

• Structural Monitoring and Maintenance
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• v.4 no.4
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• pp.331-350
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• 2017

Based on the alternative stabilization diagram by varying the time lag parameter in the stochastic subspace identification analysis, this study aims to investigate the measurements from several cases of civil structures for extending the applicability of a recently noticed criterion to ensure stable identification results. Such a criterion demands the time lag parameter to be no less than a critical threshold determined by the ratio of the sampling rate to the fundamental system frequency and is firstly validated for its applications with single measurements from stay cables, bridge decks, and buildings. As for multiple measurements, it is found that the predicted threshold works well for the cases of stay cables and buildings, but makes an evident overestimation for the case of bridge decks. This discrepancy is further explained by the fact that the deck vibrations are induced by multiple excitations independently coming from the passing traffic. The cable vibration signals covering the sensor locations close to both the deck and pylon ends of a cable-stayed bridge provide convincing evidences to testify this important discovery.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.283-294
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• 2020

In this paper, an adaptive MEMD based modal identification technique for linear time-invariant systems is proposed employing multiple vibration measurements. Traditional empirical mode decomposition (EMD) suffers from mode-mixing during sifting operations to identify intrinsic mode functions (IMF). MEMD performs better in this context as it considers multi-channel data and projects them into a n-dimensional hypercube to evaluate the IMFs. Using this technique, modal parameters of the structural system are identified. It is observed that MEMD has superior performance compared to its traditional counterpart. However, it still suffers from mild mode-mixing in higher modes where the energy contents are low. To avoid this problem, an adaptive filtering scheme is proposed to decompose the interfering modes. The Proposed modified scheme is then applied to vibrations of a reinforced concrete road bridge. Results presented in this study show that the proposed MEMD based approach coupled with the filtering technique can effectively identify the parameters of the dominant modes present in the structural response with a significant level of accuracy.