• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.101-105
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• 2018

In this paper, the kernel regression model is applied for the case study of gas turbine abnormal state analysis. In addition to vibration analysis at the remote site, the kernel regression model technique can is useful for analyzing abnormal state of rotor vibration signals of gas turbine in power plant. In monitoring based on data-driven techniques correlated measurements, the fault free training data of shaft vibration obtained during normal operations of gas turbine are used to develop a empirical model based on auto-associative kernel regression. This data-driven model can be used to predict virtual measurements, which are compared with real-time data, generating residuals. Any faults in the system may cause statistically abnormal changes in these residuals and could be detected. As the result, the kernel regression model provides information that can distinguish anomalies such as sensor failure in a shaft vibration signal.

• Journal of the Earthquake Engineering Society of Korea
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• v.13 no.3
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• pp.11-19
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• 2009

Recently, concerns relating to the maintenance of large structures have been increased. In addition, the number of large structures that need to be evaluated for their structural safety due to natural disasters and structural deterioration has been rapidly increasing. It is common for the structural characteristics of an older large structure to differ from the characteristics in the initial design stage, and changes in dynamic characteristics may result from a reduction in stiffness due to cracks on the materials. The process of deterioration of such structures enables the detection of damaged locations, as well as a quantitative evaluation. One of the typical measuring instruments used for the monitoring of bridges and buildings is the dynamic measurement system. Conventional dynamic measurement systems require considerable cabling to facilitate a direct connection between sensor and DAQ logger. For this reason, a method of measuring structural responses from a remote distance without the mounted sensors is needed. In terms of non-contact methods that are applicable to dynamic response measurement, the methods using the doppler effect of a laser or a GPS are commonly used. However, such methods could not be generally applied to bridge structures because of their costs and inaccuracies. Alternatively, a method using a visual image can be economical as well as feasible for measuring vibration signals of inaccessible bridge structures and extracting their dynamic characteristics. Many studies have been conducted using camera visual signals instead of conventional mounted sensors. However, these studies have been focused on measuring displacement response by an image processing technique after recording a position of the target mounted on the structure, in which the number of measurement targets may be limited. Therefore, in this study, a model experiment was carried out to verify the measurement algorithm for measuring multi-point displacement responses by using a DIC (Digital Image Correlation) technique.

• Journal of the Earthquake Engineering Society of Korea
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• v.1 no.1
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• pp.41-49
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• 1997

This paper presents the result of an international cooperative research on the post-correlation analysis of forced vibration tests and the prediction of earthquake responses of a large-scale seismic test structure. The dynamic analysis is carried out using the axisymmetric finite element method incorporating in finite elements for the for field soil region. Through the post-correlation analysis, the properties of the soil layers are revised so that the best correlation in the responses may be obtained compared with the measured force vibration test data. Utilizing the revised soil properties as the initial linear values, the seismic responses are predicted for an earthquake using the equivalent linearlization technique. It has been found that the predicted responses by the equivalent nonlinear procedure are in excellent agreement with the observed responses, while those using the linear properties are fairly off from the measured results.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.7
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• pp.726-735
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• 2009

In this paper, we propose an approach to signal model-based fault detection and diagnosis system for induction motors. The current fault detection techniques used in the industry are limit checking techniques, which are simple but cannot predict the types of faults and the initiation of the faults. The system consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, the system extracts the significant features from sound signals using combination of variance, cross-correlation and wavelet. Consequently, the pattern classification technique is applied to the fault diagnosis process to recognize the system faults based on faulty symptoms. The sounds generated from different kinds of typical motor's faults such as motor unbalance, bearing misalignment and bearing loose are examined. We propose two approaches for fault detection and diagnosis system that are waveletand-variance-based and wavelet-and-crosscorrelation-based approaches. The results of our experiment show more than 95 and 78 percent accuracy for fault classification, respectively.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.51-55
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• 2007

In this paper Statistical Energy Analysis has been considered to predict high frequency air borne interior noise. Dash panel Insulation is major part to reduce engine excitation noise. Transmission loss and absorption coefficient are considered to predict dash insulation performance. Transmission lose is derived from coupling loss factor and absorption coefficient is derived from internal damping loss factor. Material Biot properties were used to calculate each loss factors. Insulation geometry thickness distribution was hard to measure, so FeGate software was used to calculate thickness map from CAD drawing. Each predicted transmission losses between conventional insulation and light weight insulation were compared with SEA. Transmission loss measurement was performed to validate each prediction result, and it showed good correlation between prediction and measurement. Finally interior noise prediction was performed and result showed light weight insulation system can reduce 40% weight to keep similar performance with conventional insulation system, even though light weigh insulation system has lower sound transmission loss and higher absorption coefficient than conventional system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.04a
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• pp.483-489
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• 2009

The Time Difference of Arrival (TDOA) algorithm is being used widely for identifying the location of a source emanating either electrical or acoustic signal. It's application areas will not be limited to identifying the source at a fixed location, for example the origin of an earthquake, but will also include the trajectory monitoring for a moving source equipped with a GPS sensor. Most of the TDOA algorithm uses time correlation technique to find the time delay between received signals, and therefore difficult to be used for identifying the location of multiple sources. In this paper a TDOA algorithm based on cross-spectrum is developed to find the trajectory of two sound sources with different frequencies. Although its application is limited to for the sources on a disk plane, but it can be applied for identifying the locations of more than two sources simultaneously.

• Smart Structures and Systems
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• v.4 no.2
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• pp.209-220
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• 2008

A methodology for the seismic vulnerability assessment of historical monuments is presented in this paper. The ongoing work has been conducted in Tunisia within the framework of the FP6 European Union project (WIND-CHIME) on the use of appropriate modern seismic protective systems in the conservation of Mediterranean historical buildings in earthquake-prone areas. The case study is the five-century-old Zaouia of Sidi Kassem Djilizi, located downtown Tunis, the capital of Tunisia. Ambient vibration tests were conducted on the case study using a number of force-balance accelerometers placed at selected locations. The Enhanced Frequency Domain Decomposition (EFDD) technique was applied to extract the dynamic characteristics of the monument. A 3-D finite element model was developed and updated to obtain reasonable correlation between experimental and numerical modal properties. The set of parameters selected for the updating consists of the modulus of elasticity in each wall element of the finite element model. Seismic vulnerability assessment of the case study was carried out via three-dimensional time-history dynamic analyses of the structure. Dynamic stresses were computed and damage was evaluated according to a masonry specific plane failure criterion. Statistics on the occurrence, location and type of failure provide a general view for the probable damage level and mode. Results indicate a high vulnerability that confirms the need for intervention and retrofit.

• Bulletin of the Korean Chemical Society
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• v.19 no.4
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• pp.422-428
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• 1998

In the present paper, we report a molecular dynamics (MD) simulation of non-rigid zeolite-A framework only as the base case for a consistent study of the role of intraframework interaction on several zeolite-A systems using the same technique in our previous studies of rigid zeolite-A frameworks. Usual bond stretching, bond angle bending, torsional rotational, and non-bonded Lennard-Jones and electrostatic interactions are considered as intraframework interaction potentials. The comparison of experimental and calculated structural parameters confirms the validity of our MD simulation for zeolite-A framework. The radial distribution functions of non-rigid zeolite-A framework atoms characterize the vibrational motion of the framework atoms. Mean square displacements are all periodic with a short period of 0.08 ps and a slow change in the amplitude of the vibration with a long period of 0.53 ps. The displacement auto-correlation (DAC) and neighbor-correlation (DNC) functions describe the up-and-down motion of the framework atoms from the center of α-cage and the back-and-forth motion on each ring window from the center of each window. The DAC and DNC functions of the framework atoms from the center of α-cage at the 8-ring windows have the same period of the up-and-down motion, but those functions from the center of 8-ring window at the 8-ring windows are of different periods of the back-and-forth motion.

• Structural Engineering and Mechanics
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• v.56 no.5
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• pp.767-785
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• 2015

The stochastic finite element method is employed to obtain a stochastic dynamic model of angled beams subjected to impact loads when uncertain material properties are described by random fields. Using the perturbation technique in conjunction with a precise time integration method, a random analysis approach is developed for efficient analysis of random elastic waves. Formulas for the mean, variance and covariance of displacement, strain and stress are introduced. Statistics of displacement and stress waves is analyzed and effects of bend angle and material stochasticity on wave propagation are studied. It is found that the elastic wave correlation in the angled section is the most significant. The mean, variance and covariance of the stress wave amplitude decrease with an increase in bend angle. The standard deviation of the beam material density plays an important role in longitudinal displacement wave covariance.

• Smart Structures and Systems
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• v.3 no.3
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• pp.341-356
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• 2007

A good understanding of normal modal variability of civil structures due to varying environmental conditions such as temperature and wind is important for reliable performance of vibration-based damage detection methods. This paper addresses the quantification of wind-induced modal variability of a cable-stayed bridge making use of one-year monitoring data. In order to discriminate the wind-induced modal variability from the temperature-induced modal variability, the one-year monitoring data are divided into two sets: the first set includes the data obtained under weak wind conditions (hourly-average wind speed less than 2 m/s) during all four seasons, and the second set includes the data obtained under both weak and strong (typhoon) wind conditions during the summer only. The measured modal frequencies and temperatures of the bridge obtained from the first set of data are used to formulate temperature-frequency correlation models by means of artificial neural network technique. Before the second set of data is utilized to quantify the wind-induced modal variability, the effect of temperature on the measured modal frequencies is first eliminated by normalizing these modal frequencies to a reference temperature with the use of the temperature-frequency correlation models. Then the wind-induced modal variability is quantitatively evaluated by correlating the normalized modal frequencies for each mode with the wind speed measurement data. It is revealed that in contrast to the dependence of modal frequencies on temperature, there is no explicit correlation between the modal frequencies and wind intensity. For most of the measured modes, the modal frequencies exhibit a slightly increasing trend with the increase of wind speed in statistical sense. The relative variation of the modal frequencies arising from wind effect (with the maximum hourly-average wind speed up to 17.6 m/s) is estimated to range from 1.61% to 7.87% for the measured 8 modes of the bridge, being notably less than the modal variability caused by temperature effect.