• Corrosion Science and Technology
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• v.7 no.4
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• pp.208-211
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• 2008

Safety operation of main pipelines is primarily provided by anticorrosive monitoring. Anticorrosive monitoring of oil pipeline transportation objects is based on results of complex corrosion inspections, analysis of basic data including design data, definition of a corrosion residual rate and diagnostic of general equipment's technical condition. All the abovementioned arrangements are regulated by normative documents. For diagnostics of corrosion-technical condition of oil pipeline transportation objects one presently uses different methods such as in-line inspection using devices with ultrasonic, magnetic or another detector, acoustic-emission diagnostics, electrometric survey, general external corrosion diagnostics and cameral processing of obtained data. Results of a complex of diagnostics give a possibility: $\cdot$ to arrange a pipeline's sectors according to a degree of corrosion danger; $\cdot$ to check up true condition of pipeline's metal; $\cdot$ to estimate technical condition and working ability of a system of anticorrosive protection. However such a control of corrosion technical condition of a main pipeline creates the appearance of estimation of a true degree of protection of an object if values of protective potential with resistive component are taken into consideration only. So in addition to corrosive technical diagnostics one must define a true residual corrosion rate taking into account protective action of electrochemical protection and true protection of a pipeline one must at times. Realized anticorrosive monitoring enables to take a reasonable decision about further operation of objects according to objects' residual life, variation of operation parameters, repair and dismantlement of objects.

• The Journal of Korean Academy of Prosthodontics
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• v.41 no.6
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• pp.762-771
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• 2003

Statement of problem. In dentistry, the minimally prepared inlay resin-bonded fixed partial denture (FPD) made of new ceromer / fiber-reinforced composite (FRC) was recently introduced. However, the appropriate dimensions for the long-term success and subsequent failure strength are still unknown. Purpose. The aim of this study was to investigate the most fracture-resistible thickness combination of the ceromer / FRC using a universal testing machine and an AE analyzer. Material and Methods. A metal jig considering the dimensions of premolars and molars was milled and 56-epoxy resin dies, which had a similar elastic modulus to that of dentin, were duplicated. According to manufacturer's instructions, the FRC beams with various thicknesses (2 to 4 mm) were constructed and veneered with the 1 or 2 mm-thick ceromers. The fabricated FPDs were luted with resin cement on the resin dies and stored at room temperature for 72 hours. AE (acoustic emission) sensors were attached to both ends, the specimens were subjected to a compressive load until fracture at a crosshead speed of 0.5 mm/min. The AE and failure loads were recorded and analyzed statistically. Results. The results showed that the failure strength of the ceromer/FRC inlay FPDs was affected by the total thickness of the connectors rather than the ceromer to FRC ratio or the depth of the pulpal wall. Fracture was initiated from the interface and propagated into the ceromer layer regardless of the change in the ceromer / FRC ratio. Conclusion. Within the limitations of this study, the failure loads showed significant differences only in the case of different connector thicknesses, and no significant differences were found between the same connector thickness groups. The application of AE analysis method in a fiber-reinforced inlay FPD can be used to evaluate the fracture behavior and to analyze the precise fracture point.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.1
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• pp.39-45
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• 1999

Because of internal voids in insulators give rise to partial discharge(PD), which cause local breakdown and even entire insulation breakdown. Treeing due to PD is one of the main causes of breakdown of the insulating materials and nrluction of the insulation life. 1berefore the necessity for establishing a rrethod to diagnose the aging of insulation materials and to predict the breakdown of insulation has becorne irrportant. From this viewpoint, our studies diagnose insulation degradation using the rrethod of computer sensing system, which has PD and acoustic emission(AE) sensing system. To use advantages of these two methods can be used effectiveiy to search for treeing location and PD in sorre materials. In analysis rrethod of degradation, we analyzed the PD and AE pulses by regression analysis, corrpared to these obtained the correlation coefficient and retermination coefficient by T-distribution and saw that PD and AE pulses show a similar pattern on the whole. This has a similar teIlrency to the results of the research by Yoshimura and Fujita.Fujita.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.51-52
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• 2007

This paper compared wired Acoustic Emission (AE) signals with wireless AE signals. According to the material and process condition, each process signal has distinguishable characteristic to show each removal phenomenon. Therefore, wired and wireless AE sensors having different bandwidth are complementary for CMP process monitoring. Especially, the AE sensor was used to investigate abrasive and molecular-scale phenomena during CMP process, which was compatible to acquire high level frequency. In experiment, wireless AE system was used to get signals in rotary system, using bluetooth. But, it is possible to acquire only RMS signals, which can not analyze abrasive and molecular-sale phenomena. Second, wired AE system was installed using mercury slip-ring, which is suitable not only for rotation equipment but also for acquiring original signals. The acquired signals were analyzed by FFT for understanding of abrasive and molecular revel phenomena in CMP process, finally, we verified that two types of AE sensor with different bandwidth were complementary for CMP process monitoring.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.46-53
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• 2001

The purpose of this paper is to develop a new feature selection method for AE signal classification. The neural network of back propagation algorithm is used. The proposed feature selection method uses the difference between feature coordinates in feature space. This method is compared with the existing methods such as Fisher's criterion, class mean scatter criterion and eigenvector analysis in terms of the recognition rate and the convergence speed, using the signals from the defects in welding zone of austenitic stainless steel and in the metal contact of the rotary compressor. The proposed feature selection methods such as 2-D and 3-D criteria showed better results in the recognition rate than the existing ones.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.481-489
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• 2000

A time-frequency analysis method was developed to analyze the dispersive waves caused by impact loads in structural members such as beams and plates. Stress waves generated by ball drop and pencil lead break were recorded by ultrasonic transducers and acoustic emission (AE) sensors. Wavelet transform (WT) using Gabor function was employed to analyze the dispersive waves in the time-frequency domain, and then to find the arrival time of the waves as a function of frequency. The measured group velocities in the beam and the plate were compared with the predictions based on the Timoshenko beam theory and Rayleigh-Lamb frequency equations, respectively. The agreements were found to be very good.

• Geomechanics and Engineering
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• v.24 no.2
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• pp.179-191
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• 2021

In this study, the strength and failure mechanism of red sandstones with combined defects were investigated by uniaxial compression tests on red sandstones with different crack angles using two-dimensional particle flow code numerical software, and their mechanical parameters and failure process were studied and analyzed. The results showed that the mechanical characteristics such as peak strength, peak strain, and elastic modulus of the samples with prefabricated combined defects were significantly inferior than those of the intact samples. With increasing crack angle from 15° to 60°, the weakening area of cracks increased, elastic modulus, peak strength, and peak strain gradually reduced, the total number of cracks increased, and more strain energy was released. In addition, the samples underwent initial brittle failure to plastic failure stage, and the failure form was more significant, leading to peeling phenomenon. However, with increasing crack angle from 75° to 90°, the crack-hole combination shared the stress concentration at the tip of the crack-crack combination, resulted in a gradual increase in elastic modulus, peak strain and peak strength, but a decrease in the number of total cracks, the release of strain energy reduced, the plastic failure state weakened, and the spalling phenomenon slowed down. On this basis, the samples with 30° and 45° crack-crack combination were selected for further experimental investigation. Through comparative analysis between the experimental and simulation results, the failure strength and final failure mode with cracks propagation of samples were found to be relatively similar.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.121-126
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• 2020

Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2107-2119
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• 2022

Accurate remaining useful life (RUL) prediction for critical components of nuclear power equipment is an important way to realize aging management of nuclear power equipment. The electric gate valve is one of the most safety-critical and widely distributed mechanical equipment in nuclear power installations. However, the electric gate valve's extended service in nuclear installations causes aging and degradation induced by crack propagation and leakages. Hence, it is necessary to develop a robust RUL prediction method to evaluate its operating state. Although the particle filter(PF) algorithm and its variants can deal with this nonlinear problem effectively, they suffer from severe particle degeneracy and depletion, which leads to its sub-optimal performance. In this study, we combined the whale algorithm with regularized particle filtering(RPF) to rationalize the particle distribution before resampling, so as to solve the problem of particle degradation, and for valve RUL prediction. The valve's crack propagation is studied using the RPF approach, which takes the Paris Law as a condition function. The crack growth is observed and updated using the root-mean-square (RMS) signal collected from the acoustic emission sensor. At the same time, the proposed method is compared with other optimization algorithms, such as particle swarm optimization algorithm, and verified by the realistic valve aging experimental data. The conclusion shows that the proposed method can effectively predict and analyze the typical valve degradation patterns.

• Geomechanics and Engineering
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• v.30 no.1
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• pp.93-105
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• 2022

With the exploitation of natural resources in China, underground resource extraction and underground space development, as well as other engineering activities are increasing, resulting in the creation of many defective rocks. In this paper, uniaxial compression tests were performed on rocks with double holes and fractures at different angles using particle flow code (PFC2D) numerical simulations and laboratory experiments. The failure behavior and mechanical properties of rock samples with holes and fractures at different angles were analyzed. The failure modes of rock with defects at different angles were identified. The fracture propagation and stress evolution characteristics of rock with fractures at different angles were determined. The results reveal that compared to intact rocks, the peak stress, elastic modulus, peak strain, initiation stress, and damage stress of fractured rocks with different fracture angles around holes are lower. As the fracture angle increases, the gap in mechanical properties between the defective rock and the intact rock gradually decreased. In the force chain diagram, the compressive stress concentration range of the combined defect of cracks and holes starts to decrease, and the model is gradually destroyed as the tensile stress range gradually increases. When the peak stress is reached, the acoustic emission energy is highest and the rock undergoes brittle damage. Through a comparative study using laboratory tests, the results of laboratory real rocks and numerical simulation experiments were verified and the macroscopic failure characteristics of the real and simulated rocks were determined to be similar. This study can help us correctly understand the mechanical properties of rocks with defects and provide theoretical guidance for practical rock engineering.