• Wind and Structures
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• v.29 no.3
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• pp.209-223
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• 2019

The assessment of wind-induced vibration for tall reinforced concrete (RC) buildings requires the accurate estimation of their dynamic properties, e.g., the fundamental vibration periods and damping ratios. In this study, RC frame-shear wall systems designed under gravity and wind loadings have been evaluated by utilising 3D FE modelling incorporating eigen-analysis to obtain the elastic periods of vibration. The conducted parameters consist of the number of storeys, the plan aspect ratio (AR) of buildings, the core dimensions, the space efficiency (SE), and the leasing depth (LD) between the internal central core and outer frames. This analysis provides a reliable basis for further investigating the effects of these parameters and establishing new formulas for predicting the fundamental vibration periods by using regression analyses on the obtained results. The proposed constrained numerically based formula for vibration periods of tall RC frame-shear wall office buildings in terms of the height of buildings reasonably agrees with some cited formulas for vibration period from design codes and standards. However, the same proposed formula has a high discrepancy with other cited formulas from the rest of design codes and standards. Also, the proposed formula agrees well with some cited experimentally based formulas.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.11
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• pp.1192-1201
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• 2009

Analysis of the vibration characteristic for cylindrical shell is more complex than plates since the coupling effects are considered on three dimensions. Based on Love's equation, spectral finite element method(SFEM) is introduced to predict frequency response function of finite circular cylindrical shell in the air with simply supported - free boundary condition without simplifying the equation of motion. And for the radiated noise analysis of cylindrical shell, indirect boundary element method(BEM) is applied using out-of-plane displacements as an input from structural vibration analysis. Comparisons of the structural vibration results by the spectral finite element method and commercial code, NASTRAN(FEM based) are carried out. Likewise, for verification of radiated noise analysis results, commercial code, SYSNOISE(BEM based) are used.

• Smart Structures and Systems
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• v.15 no.3
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• pp.787-806
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• 2015

Due to corrosion, a large number of belt conveyors support structure in industrial plants have deteriorated. Severe corrosion may result in collapse of the structures. Therefore, practical and effective structural assessment techniques are needed. In this paper, damage identification methods based on two specific local vibration modes, named periodic and isolated local vibration modes, are proposed. The identification methods utilize the facts that support structures have many identical members repeated along the belt conveyor and there exist some local modes within a small frequency range where vibrations of these identical members are much larger than those of the other members. When one of these identical members is damaged, this member no longer vibrates in those modes. Instead, the member vibrates alone in an isolated mode with a lower frequency. A damage identification method based on frequencies comparison of these vibration modes and another method based on amplitude comparison of the periodic local vibration mode are explained. These methods do not require the baseline measurement records of undamaged structure. The methods is capable of detecting multiple damages simultaneously. The applicability of the methods is experimentally validated with a laboratory model and a real belt-conveyor support structure.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.6_spc
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• pp.714-720
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• 2016

Rubbing and misalignment are the most usual faults that occurs in rotating machinery and with them severe effect on power plant availability. Especially blade rubbing is hard to detect on FFT spectrum using the vibration signal. In this paper, the possibility of feature analysis of vibration signal is confirmed under blade rubbing and misalignment condition. And the lab-scale rotor test device provides the blade rubbing and shaft misalignment modes. Feature selection based on GA (genetic algorithm) is processed by the extracted feature of the time domain. Then, classification of the features is analyzed by using SVM (support vector machine) which is one of the machine learning algorithm. The results of features selection based on GA compared with those based on PCA (principal component analysis). According to the results, the possibility of feature analysis is confirmed. Therefore, blade rubbing and shaft misalignment can be diagnosed by feature of vibration signal.

• The KIPS Transactions:PartA
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• v.17A no.2
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• pp.81-92
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• 2010

There has been some research in the equipment defect detection based on vibration information. Most research of them is based on vibration monitoring to determine the equipment defect or not. In this paper, we introduce more accurate system for engine defect detection based on vibration information and we focus on detection of engine defect for boat and system control. First, it uses the duplicated-checking method for vibration information to determine the engine defect or not. If there is a defect happened, we use the method using error part of vibration information basis with error range to determine which kind of error is happened. On the other hand, we use the engine trend analysis and standard of safety engine to implement the vibration information database. Our simulation results show that the probability of engine defect determination is 100% and the probability of engine defect classification and detection is 96%.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.263-270
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• 2003

This paper deals with a control technique of eliminating the transient vibration of a waist axis of an articulated robot. This technique is based on a model-based control in order to establish the damping effect on the mechanical part. The control model is related to the velocity control loop, and it is composed of reduced-order electrical and mechanical parts. Using this model, the velocity of the load is estimated, which is converted to the motor shaft. The difference between the estimated load speed and the motor speed is calculated dynamically, and it is added to the velocity command to suppress the transient vibration of a waist axis of the robot arm. The function of this technique is to increase the cut-off frequency of the system and the damping ratio at the driven machine part. This control model is easily obtained from design or experimental data and its algorithm can be easily installed in a DSP. This control technique is applied to a waist axis of an articulated robot composed of a harmonic drive gear reducer and a robot arm with 5 degrees of freedom. Simulations and experiments show satisfactory control results to reduce the transient vibration at the end-effector.

• Smart Structures and Systems
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• v.15 no.6
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• pp.1625-1642
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• 2015

We propose an electrically self-reliant structural health monitoring (SHM) system that is able to wirelessly transmit sensing data using electrical power generated by vibration without the need for additional external power sources. The provision of reliable electricity to wireless SHM systems is a highly important issue that has often been ignored, and to expand the applicability of various wireless SHM innovations, it will be necessary to develop comprehensive wireless SHM devices including stable electricity sources. In light of this need, we propose a new, highly efficient vibration-powered generator based on a tuned-mass-damper (TMD) mechanism that is quite suitable for vibration-based SHM. The charging time of the TMD generator is shorter than that of conventional generators based on the impedance matching method, and the proposed TMD generator can harvest 16 times the amount of energy that a conventional generator can. The charging time of an SHM wireless transmitter is quantitatively formulated. We conduct wireless monitoring experiments to validate a wireless SHM system composed of a self-reliant SHM and a vibration-powered TMD generator.

• Tunnel and Underground Space
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• v.28 no.6
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• pp.547-554
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• 2018

Using crowdsourced sensor data collection technique, it is possible to collect high-density ground vibration data which is difficult to obtain by conventional methods. In this study, we have developed a crowdsourced ground vibration data collection system using MEMS sensors mounted on small electronic devices including smartphones, and implemented client and server based on the proposed infrastructure system design. The system is designed to gather vibration data quickly through Android-based smartphones or fixed devices based on Android Things, minimizing the usage of resource like power usage and data transmission traffic of the hardware.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.223-235
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• 2021

The Vortex-Induced Vibration (VIV) test system on deepwater riser based on Bare Fiber Bragg Grating (BFBG) sensor technology was designed. Meanwhile, a riser VIV response numerical model was established based on the work-energy principle. The results show that the first-order vibration frequency dominates the vibration of the riser, and as the velocity increases, the dominant frequency of the riser gradually increases under the effect of different top tensions. At the same velocity, as the top tension increases step by step, the dominant frequency and fatigue damage at the same position along the axial length of the riser both gradually decreases. The model test and numerical simulation show a relatively consistent change, maintaining a high degree of agreement. The process control system based on BFBG of model test has excellent performance, and FBG sensors have great advantages in VIV test of a vertical riser in water.

• Smart Structures and Systems
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• v.32 no.1
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.