• Wind and Structures
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• 제36권2호
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• pp.133-147
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• 2023

The random vibration of saddle membrane structures under wind load is studied theoretically and experimentally. First, the nonlinear random vibration differential equations of saddle membrane structures under wind loads are established based on von Karman's large deflection theory, thin shell theory and potential flow theory. The probabilistic density function (PDF) and its corresponding statistical parameters of the displacement response of membrane structure are obtained by using the diffusion process theory and the Fokker Planck Kolmogorov equation method (FPK) to solve the equation. Furthermore, a wind tunnel test is carried out to obtain the displacement time history data of the test model under wind load, and the statistical characteristics of the displacement time history of the prototype model are obtained by similarity theory and probability statistics method. Finally, the rationality of the theoretical model is verified by comparing the experimental model with the theoretical model. The results show that the theoretical model agrees with the experimental model, and the random vibration response can be effectively reduced by increasing the initial pretension force and the rise-span ratio within a certain range. The research methods can provide a theoretical reference for the random vibration of the membrane structure, and also be the foundation of structural reliability of membrane structure based on wind-induced response.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.12-22
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• 2007

This paper presents to predict the powertrain structure-borne noise which is primary resource of interior noise. As the first step, it is built up a hybrid powertrain model which is based on the real powertrain which is verified with static and dynamic properties. The methods for verifying are modal analysis and running vibration testing which are experimentally implemented. Based on the Hybrid powertrain component model, an initial predictive assembly model is simulated. As the second step, the characteristic transfer functions are measured that are dynamic stiffness of rubber mounts and vibro-acoustic transfer function based on the acoustic reciprocity. Several techniques utilizing special experimental devices have been proposed for this research. Finally, the structure-borne noise by powertrain will be predict and verify with dynamic simulation and experiment.

• 한국소음진동공학회논문집
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• 제21권3호
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• pp.199-205
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• 2011

This paper proposes a framework to predict the mechanical fault of hot rolling mill system (HRMS). The optimum process of HRMS is usually identified by the rotating velocity of working roll. Therefore, observing the velocity of working roll is relevant to early know the HRMS condition. In this paper, we propose the framework which consists of two methods namely spectrum matrix which related to case-based fast Fourier transform(FFT) analysis, and three dimensional condition monitoring based on novel visualization. Validation of the proposed method has been conducted using vibration data acquired from HRMS by accelerometer sensors. The acquired data was also tested by developed software referred as hot rolling mill facility analysis module. The result is plausible and promising, and the developed software will be enhanced to be capable in prediction of remaining useful life of HRMS.

• 제어로봇시스템학회논문지
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• 제18권12호
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• pp.1090-1095
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• 2012

In this paper, we propose a model-based fault diagnosis method applied to vibration data. The fault detection is performed by comparing estimated parameters with normal parameters and deciding if the observed changes can be explained satisfactorily in terms of noise or undermodelling. The key feature of this method is that it accounts for the effects of noise and model mismatch. And we aslo design a classifier for the fault isolation by applying the multiclass SVM (Support Vector Machine) to the estimated parameters. The proposed fault detection and isolation methods are applied to an engine vibration data to show a good performance. The proposed fault detection method is compared with a signal-based fault detection method through a performance analysis.

• Structural Engineering and Mechanics
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• 제67권3호
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• pp.311-316
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• 2018

Vibration of concrete beams reinforced by agglomerated silicon dioxide ($SiO_2$) nanoparticles is studied based on numerical methods. The structure is simulated by Euler-Bernoulli beam model and the Mori-Tanaka model is used for obtaining the effective material properties of the structure. The concrete beam is located in soil medium which is modeled by spring elements. The motion equations are derived based on energy method and Hamilton's principle. Based on exact solution, the frequency of the structure is calculated. The effects of different parameters such as volume percent of $SiO_2$ nanoparticles and agglomeration, soil medium and geometrical parameters of beam are shown on the frequency of system. The results show that with increasing the volume percent of $SiO_2$ nanoparticles, the frequency increases.

• Journal of Information Processing Systems
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• 제16권3호
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• pp.639-647
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• 2020

In the vibration target localization algorithms based on time difference of arrival (TDOA), Fang algorithm is often used in practice because of its simple calculation. However, when the delay estimation error is large, the localization equation of Fang algorithm has no solution. In order to solve this problem, one dimensional search location algorithm based on TDOA is proposed in this paper. The concept of search is introduced in the algorithm. The distance d1 between any single sensor and the vibration target is considered as a search variable. The vibration target location is searched by changing the value of d1 in the two-dimensional plane. The experiment results show that the proposed algorithm is superior to traditional methods in localization accuracy.

• KEPCO Journal on Electric Power and Energy
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• 제4권2호
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• pp.89-99
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• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.

• 상하수도학회지
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• 제35권5호
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• pp.335-349
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• 2021

As the frequency of seismic disasters in Korea has increased rapidly since 2016, interest in systematic maintenance and crisis response technologies for structures has been increasing. A data-based leading management system of Lifeline facilities is important for rapid disaster response. In particular, the water supply network, one of the major Lifeline facilities, must be operated by a systematic maintenance and emergency response system for stable water supply. As one of the methods for this, the importance of the structural health monitoring(SHM) technology has emerged as the recent continuous development of sensor and signal processing technology. Among the various types of SHM, because all machines generate vibration, research and application on the efficiency of a vibration-based SHM are expanding. This paper reviews a vibration-based pipeline SHM system for seismic disaster response of water supply pipelines including types of vibration sensors, the current status of vibration signal processing technology and domestic major research on structural pipeline health monitoring, additionally with application plan for existing pipeline operation system.

• 한국정밀공학회지
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• 제25권12호
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• pp.107-116
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• 2008

The excitation force of a powertrain is one of major sources for the interior noise of a vehicle. This paper presents a novel approach to predict the interior noise caused by the vibration of the power rain by using the hybrid TPA (transfer path analysis) method. Although the traditional transfer path analysis (TPA) is useful for the identification of powertrain noise sources, it is difficult to modify the structure of a powertrain by using the experimental method for the reduction of vibration and noise. In order to solve this problem, the vibration of the power rain in a vehicle is numerically analyzed by using the finite element method (FEM). The vibration of the other parts in a vehicle is investigated by using the experimental method based on vibrato-acoustic transfer function (VATF) analysis. These two methods are combined for the prediction of interior noise caused by a power rain. Throughout this research, two papers are presented. This paper presents a simulation of the excitation force of the power rain exciting the vehicle body based on numerical simulation. The other paper presents a prediction of interior noise based on the hybrid TPA, which uses the VATF of the car body and the excitation force predicted in this paper.

• Smart Structures and Systems
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• 제30권6호
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• pp.557-569
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• 2022

Vibration-based structural health monitoring (SHM) is crucial for the dynamic maintenance of civil building structures to protect property security and the lives of the public. Analyzing these vibrations with modern artificial intelligence and deep learning (DL) methods is a new trend. This paper proposed an unsupervised deep learning method based on a convolutional autoencoder (CAE), which can overcome the limitations of conventional supervised deep learning. With the convolutional core applied to the DL network, the method can extract features self-adaptively and efficiently. The effectiveness of the method in detecting damage is then tested using a benchmark model. Thereafter, this method is used to detect damage and instant disaster events in a rubber bearing-isolated gymnasium structure. The results indicate that the method enables the CAE network to learn the intact vibrations, so as to distinguish between different damage states of the benchmark model, and the outcome meets the high-dimensional data distribution characteristics visualized by the t-SNE method. Besides, the CAE-based network trained with daily vibrations of the isolating layer in the gymnasium can precisely recover newly collected vibration and detect the occurrence of the ground motion. The proposed method is effective at identifying nonlinear variations in the dynamic responses and has the potential to be used for structural condition assessment and safety warning.