• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.415-418
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• 2011

Thermal fatigue is one of the life-limiting damage mechanisms in the nuclear power plant conditions. The turbulent mixing of fluids of different temperatures induces rapid temperature changes to the pipe wall. The successive thermal transients cause varying cyclic thermal stresses. These cyclic thermal stresses cause fatigue crack nucleation and growth similar to the cyclic mechanical stresses. The aim of this study was to fulfil the need by developing an real crack manufacturing method, which would produce realistic cracks. The test material was austenitic STS 304, which is used as pipelines in the reactor coolant system of a nuclear power plants. In order to fabricate thermal fatigue crack similar to realistic crack, successive thermal transients were applied to the specimen. Thermal transient cycles were combined with heating (60sec) and cooling cycle (30sec). And, In order to identify ultrasonic characteristic, it was performed the ultrasonic reflection measuring method for the fabricated specimen. From the results of ultrasonic reflection measuring testing, it was conformed that A-scan results(average 83% of real crack depth) for the TFC reference specimen was more enhanced NDT reliability than results(average 38% of real crack depth) for the EDM notch reference specimen.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.887-895
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• 2014

Drowsy driving is a large proportion of the total car accidents. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.

• Proceedings of the Optical Society of Korea Conference
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• 2006.07a
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• pp.153-154
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• 2006

• Journal of Ocean Engineering and Technology
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• v.35 no.5
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• pp.327-335
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• 2021

Microcracks can rapidly grow and develop in high-strength steels used in offshore structures. It is important to render these microcracks harmless to ensure the safety and reliability of offshore structures. Here, the dependence of the aspect ratio (As) of the maximum depth of harmless crack (a_hlm) was evaluated under three different conditions considering the threshold stress intensity factor (Δk_th) and residual stress of offshore structural steel F690. The threshold stress intensity factor and fatigue limit of fatigue crack propagation, dependent on crack dimensions, were evaluated using Ando's equation, which considers the plastic behavior of fatigue and the stress ratio. a_hlm by peening was analyzed using the relationship between Δk_th obtained by Ando's equation and Δk_th obtained by the sum of applied stress and residual stress. The plate specimen had a width 2W = 12 mm and thickness t = 20 mm, and four value of As were considered: 1.0, 0.6, 0.3, and 0.1. The a_hlm was larger as the compressive residual stress distribution increased. Additionally, an increase in the values of As and Δk_th(l) led to a larger a_hlm. With a safety factor (N) of 2.0, the long-term safety and reliability of structures constructed using F690 can be secured with needle peening. It is necessary to apply a more sensitive non-destructive inspection technique as a non-destructive inspection method for crack detection could not be used to observe fatigue cracks that reduced the fatigue limit of smooth specimens by 50% in the three types of residual stresses considered. The usefulness of non-destructive inspection and non-damaging techniques was reviewed based on the relationship between a_hlm, a_NDI (minimum crack depth detectable in non-destructive inspection), a_{cr N} (crack depth that reduces the fatigue limit to 1/N), and As.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.5
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• pp.485-492
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• 2009

The thermal fatigue crack(TFC) is one of the life-limiting mechanisms at the nuclear power plant operating conditions. In order to evaluate the structural integrity, various non-destructive test methods such as radiographic test, ultrasonic test and eddy current are used in the industrial field. However, these methods have restrictions that defect detection is possible after the crack growth. For this reason, acoustic emission testing(AET) is becoming one of powerful inspection methods, because AET has an advantage that possible to monitor the structure continuously. Generally, every mechanism that affects the integrity of the structure or equipment is a source of acoustic emission signal. Therefore the noise filtering is one of the major works to the almost AET researchers. In this study, acoustic emission signal was collected from the pipes which were in the successive thermal fatigue cycles. The data were filtered based on the results from previous experiments. Through the data analysis, the signal characteristics to distinguish the effective signal from the noises for the TFC were proven as the waveform difference. The experiment results provide preliminary information for the acoustic emission technique to the continuous monitoring of the structure failure detection.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2001.05a
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• pp.250-253
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• 2001

Recently, based on the smart structure concept, optical fiber sensors have been increasingly applied to monitor the various engineering and civil structural components. Repairs based on adhesively bonded fiber reinforce composite patches are more structurally efficient and much less damaging to the parent structure than standard repairs based on mechanically fastened metallic patches. As a result of the high reinforcing efficiency of bonded patches fatigue cracks can be successfully repaired. However, when such repairs are applied to primary structures, it is needed to demonstrate that its loss can be immediately detected. This approach is based on the "smart patch" concept in which the patch system monitors its own health. The objective of this study is to evaluate the potentiality of application of transmission-type extrinsic Fabry-Perot optical fiber sensor (TEFPI) to the monitoring of crack growth behavior of composite patch repaired structures. The sensing system of TEFPI and the data reduction principle for the detection of crack detection are presented. Finally, experimental results from the tests of center-cracked-tension aluminum specimens repaired with bonded composite patch is presented and discussed.

• Smart Structures and Systems
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• v.12 no.2
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• pp.97-119
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• 2013

This paper presents numerical and experimental results on the use of guided waves for structural health monitoring (SHM) of crack growth during a fatigue test in a thick steel plate used for civil engineering application. Numerical simulation, analytical modeling, and experimental tests are used to prove that piezoelectric wafer active sensor (PWAS) can perform active SHM using guided wave pitch-catch method and passive SHM using acoustic emission (AE). AE simulation was performed with the multi-physic FEM (MP-FEM) approach. The MP-FEM approach permits that the output variables to be expressed directly in electric terms while the two-ways electromechanical conversion is done internally in the MP-FEM formulation. The AE event was simulated as a pulse of defined duration and amplitude. The electrical signal measured at a PWAS receiver was simulated. Experimental tests were performed with PWAS transducers acting as passive receivers of AE signals. An AE source was simulated using 0.5-mm pencil lead breaks. The PWAS transducers were able to pick up AE signal with good strength. Subsequently, PWAS transducers and traditional AE transducer were applied to a 12.7-mm CT specimen subjected to accelerated fatigue testing. Active sensing in pitch catch mode on the CT specimen was applied between the PWAS transducers pairs. Damage indexes were calculated and correlated with actual crack growth. The paper finishes with conclusions and suggestions for further work.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.4
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• pp.352-357
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• 2008

The detection of micro cracks in materials at the early stage of fracture is important in many structural safety assurance problems. The nonlinear ultrasonic technique (NUT) has been considered as a positive method for this, since it is more sensitive to micro crack than conventional linear ultrasonic methods. The basic principle is that the waveform is distorted by nonlinear stress-displacement relationship on the crack interface when the ultrasonic wave transmits through, and resultantly higher order harmonics are generated. This phenomenon is called the contact acoustic nonlinearity (CAN). The purpose of this paper is to prove the applicability of CAN experimentally by detection of micro fatigue crack artificailly initiated in Aluminum specimen. For this, we prepared fatigue specimens of Al6061 material with V-notch to initiate the crack, and the amplitude of second order harmonic was measured by scanning along the crack direction. From the results, we could see that the harmonic amplitude had good correlation with COD and it can be used to detect the crack depth in more accurately than the common 6 dB drop echo method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.30-39
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• 2012

In this paper, a safe driving assistance system is proposed by detecting the status of driver's doze based on face and eye detection. By the level of the fatigue, safe driving system alarms or set the seatbelt on vibration. To reduce the effect of backward light and too strong solar light which cause a decrease of face and eye detection rate and false fatigue detection, post processing techniques like image equalization are used. Haar transform and PCA are used for face detection. By using the statistic of the face and eye structural ratio of normal Koreans, we can reduce the eye candidate area in the face, which results in reduction of the computational load. We also propose a new eye status detection algorithm based on Hough transform and eye width-height ratio, which are used to detect eye's blinking status which decides doze level by measuring the blinking period. The system alarms and operates seatbelt on vibration through controller area network(CAN) when the driver's doze level is detected. In this paper, four algorithms are implemented and proposed algorithm is made based on the probability model and we achieves 84.88% of correct detection rate through indoor and in-car environment experiments. And also we achieves 69.81% of detection rate which is better result than that of other algorithms using IR camera.