• Proceedings of the Korean Geotechical Society Conference
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• 2001.03a
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• pp.245-248
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• 2001

During the core drilling for the design of a railway bridge crossing over the inferred fault system along the river, fracture zone, extends vertically more than the bottom of borehole, filled with fault gouge was found. The safety of bridge could be threatened by the excessive subsidence or the reduced bearing capacity of bedrock, if a fault would be developed under or around the pier foundation. Thus, a close examination of the fault was required to rearrange pier locations away from the fault or to select a reinforcement method if necessary. Geophysical methods, seismic reflection method and electrical resistivity survey over the water covered area, were applied to delineate the weak zone associated with the fault system. The results of geophysical survey clearly showed a number of faults extending vertically more than 50m. Reinforcement was not desirable because of the high cost and the water contamination, etc. The pier locations were thus rearranged based on the results of geophysical surveys to avoid the undesirable situations, and additional core drillings on the rearranged pier locations were carried out. The bedrock conditions at the additional drilling sites turned out to be acceptable for the construction of piers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1610-1629
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• 2021

Failures frequently occurred in manufacturing machines due to complex and changeable manufacturing environments, increasing the downtime and maintenance costs. This manuscript develops a novel deep learning-based method named Multi-Domain Convolutional Neural Network (MDCNN) to deal with this challenging task with vibration signals. The proposed MDCNN consists of time-domain, frequency-domain, and statistical-domain feature channels. The Time-domain channel is to model the hidden patterns of signals in the time domain. The frequency-domain channel uses Discrete Wavelet Transformation (DWT) to obtain the rich feature representations of signals in the frequency domain. The statistic-domain channel contains six statistical variables, which is to reflect the signals' macro statistical-domain features, respectively. Firstly, in the proposed MDCNN, time-domain and frequency-domain channels are processed by CNN individually with various filters. Secondly, the CNN extracted features from time, and frequency domains are merged as time-frequency features. Lastly, time-frequency domain features are fused with six statistical variables as the comprehensive features for identifying the fault. Thereby, the proposed method could make full use of those three domain-features for fault diagnosis while keeping high distinguishability due to CNN's utilization. The authors designed massive experiments with 10-folder cross-validation technology to validate the proposed method's effectiveness on the CWRU bearing data set. The experimental results are calculated by ten-time averaged accuracy. They have confirmed that the proposed MDCNN could intelligently, accurately, and timely detect the fault under the complex manufacturing environments, whose accuracy is nearly 100%.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.513-514
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• 2010

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.239-240
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• 2010

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.314-315
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• 2013

• Korean Journal of Mineralogy and Petrology
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• v.35 no.3
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• pp.377-386
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• 2022

In addition to anthropogenic origins, fluorine (F) is naturally enriched in rocks due to geological events, such as magma dissemination, hydrothermal alteration, mineralization, and fault activities. Generally, it has been well known that F is chiefly enriched in the region of igneous and metamorphic rocks, and biotite granite was mostly distributed in the study area. The F enrichment mechanism was not sufficiently elucidated in the previous studies, and the study on a fault zone was conducted to reveal it more precisely. The mineral composition of the fault zone was identical to that of the Eulwangsan biotite granite (EBG), but they were quantitatively different between the two areas. Compared with the EBG, the fault zone showed relatively higher contents of quartz and F-bearing minerals (fluorite, sericite) but lower contents of plagioclase and alkali feldspar. This difference was likely due to hydrothermal mineral alterations. The results of microscopic observations supported this, and the generation of F-bearing minerals by hydrothermal alterations was recognized in most samples. Accordingly, it might be interpreted that the mineralogical and petrological differences observed in the same-age biotite granite widely distributed in the Yongyudo was caused by the hydrothermal alterations due to small-scale geological events.

• Journal of IKEEE
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• v.26 no.2
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• pp.319-328
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• 2022

Bearing plays an important role in the operation of most machinery, Therefore, when a defect occurs in the bearing, a fatal defect throughout the machine is generated. In this reason, bearing defects should be detected early. In this paper, we describe a method using Convolutional Neural Networks (SN-CNNs) based on continuous wavelet transformations and Switchable normalization for bearing defect detection models. The accuracy of the model was measured using the Case Western Reserve University (CWRU) bearing dataset. In addition, batch normalization methods and spectrogram images are used to compare model performance. The proposed model achieved over 99% testing accuracy in CWRU dataset.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.12
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• pp.1287-1294
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• 2004

Hub bearings not only sustain the body of a cat, but permit wheels to rotate freely. Excessive radial or axial load and many other reasons can cause defects to be created and grown in each component. Therefore, nitration and noise from unwanted defects in outer-race, inner-race or ball elements of a Hub bearing are what we want to detect as early as possible. How early we can detect the faults has to do with how the detection algorithm finds the fault information from measured signal. Fortunately, the bearing signal has Periodic impulse train. This information allows us to find the faults regardless how much noise contaminates the signal. This paper shows the basic signal processing idea and experimental results that demonstrate how good the method is.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.593-596
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• 2004

Hub bearings not only sustain the body of a car, but permit wheels to rotate freely. Excessive radial or axial load and many other reasons can cause defects to be created and grown in each component. Therefore, vibration and noise from unwanted defects in outer-race, inner-race or ball elements of a Hub bearing are what we want to detect as early as possible. How early we can detect the faults has to do with how the detection algorithm finds the fault information from measured signal. Fortunately, the bearing signal has periodic impulse train. This information allows us to find the faults regardless how much noise contaminates the signal. This paper shows the basic signal processing idea and experimental results that demonstrate how good the method is.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.825-833
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• 2020

Currently, the K-Water uses various valves that can be remotely controlled for optimal water management. Valve system fault can be classified into rotor defects, stator defects, bearing defects, and gear defects of induction motors. If the valve cannot be operated due to a gear fault, the water management operation can be greatly affected. For effective water management, there is an urgent need for preemptive repairs to determine whether gear is damaged through failure prediction diagnosis.. Recently, deep learning algorithms are being applied for valve failure diagnosis. However, the method currently applied has a disadvantage of attaching a vibration sensor to the valve. In this paper, propose a new algorithm to determine whether a fault exists using a convolutional neural network (CNN) based on the voltage and current information of the valve without additional sensor mounting. In particular, a normalized Lisasjous diagram was used to maximize the fault classification performance in the CNN-based diagnostic system.